Identifying Risk Factors and Preferred Hospitals for Hip/Knee Replacements: An Analysis of 2019-2022 Hospital Readmission Reduction Program Data
Adeline Casali and Scott Eugley
2024-07-09
Background and Question
Since 2012, the Centers for Medicare and Medicaid Services have implemented the Hospital Readmission Reduction Program (HRRP). This program tracks hospital readmission rates and incentivizes hospitals to reduce unnecessary readmissions through financial penalties. Using the 2019-2022 readmission data from the HRRP, this analysis aims to identify the preferred and non-preferred hospitals for hip and knee replacements for a health insurance company. Furthermore, it will examine the risk factors associated with higher readmission rates for these procedures.
Question:
What risk factors are associated with hospital readmission rates for hip/knee replacements?
Motivation:
Understanding these risk factors can help health insurance companies guide patients towards hospitals with better outcomes, thereby improving patient outcomes and reducing costs associated with readmissions.
Need:
The insights from this analysis can be used to improve hospital performance, enhance patient care, and reduce costs. As of 2019, the average cost of readmission after hip/knee surgery was $8,588, and avoiding that cost would be highly beneficial for health insurance companies and consumers alike (Phillips et al., 2019).
Novelty:
Previous analyses have used these same or similar datasets with Logistic Regression and Random Forest models to identify the most important risk factors as they pertain to hospital readmission rates for hip/knee replacements. We will be trying to improve on this type of analysis by improving the performance of the models using various techniques. Prior analyses have implemented Random Forest models to extract important risk factors, but no prior analyses have used Random Forest to classify hospitals as preferred or non-preferred for hip/knee replacement, based on the important risk factors.
Hypothesis:
Hospitals with better Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores will have lower readmission rates for hip/knee replacements because higher patient satisfaction often correlates with better overall care quality and patient outcomes, including reduced complications and better post-discharge support (Edwards et al., 2015).
Data and Analysis
We will be using the datasets from the Centers for Medicare and Medicaid Services (Centers for Medicare & Medicaid Services, 2024). Our target variable will be the readmission rate after hip/knee surgery, using data from 2019-2022. We will utilize predictors from the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) dataset as well as Timely and Effective Care, containing information on average wait times and vaccination compliance, Complications and Deaths, containing information about the frequency of deaths and complications for procedures, and Payment and Spending metrics, which includes the costs associated with procedures.
Analysis Plan:
- Data Preprocessing:
o Handle missing values, outliers, and inconsistencies in the dataset.
o Exploratory data analysis will be performed.
- Model Selection:
o Random Forest: This model will be used to determine the most significant risk factors associated with hospital readmission, post hip/knee replacement surgery. The model will then be used in a binary classification task, which will categorize hospitals as either “preferred” or “non-preferred”, based on the national average for hospital readmission post hip/knee surgery.
o Elastic Net: While prior analyses have used coefficients from Logistic Regression modeling to determine significant risk factors, this analysis will leverage L1 and L2 regularization via an Elastic Net model.
o Neural Network: After using Elastic Net and Random Forest to find significant risk factors, these risk factors will be utilized in a neural network model and used to classify hospitals as either “preferred” or “non-preferred”. Classification ability of the neural network model will then be compared with the classification ability of the Random Forest model.
- Feature Importance Analysis:
o Identify which predictors significantly influence hospital readmission rate, post hip/knee replacement surgery.
o Linear regression analysis (Elastic Net) significant risk factors will be compared to tree-based analysis (Random Forest) significant risk factors for overlap.
- Model Validation:
o Validate the model using cross-validation techniques (k-fold, nested) to ensure robustness and generalizability.
o Hyperparameter tuning will be performed. Assessment of the model’s performance will be based on accuracy, AUC, and ROC metrics.
o The 2024 data will be used as the test set for this analysis.
Assessment:
Successful Analysis:
We will consider our analysis successful if we can identify clear risk factors associated with hospital readmission rates and accurately classify hospitals as preferred or non-preferred.
Hypothesis Support:
Our hypothesis will be supported if hospitals with better HCAHPS scores demonstrate statistically significantly lower readmission rates for hip/knee replacements.
Pitfalls:
A potential pitfall of our analysis plan is data quality and completeness. The dataset does contain missing values, and it will need to be preprocessed to handle these missing values, outliers, and inconsistencies. Another potential pitfall is not having adequate computing power to implement deep learning with the size of our dataset. Lastly, a pitfall that we need to keep an eye out for is overfitting. We will know we have overfitting if the train set far outperforms the test set, in terms of model accuracy.
Exploratory Data Analysis
Data loading and preprocessing
Loading the Data (AC)
Note: Data must be saved to personal computer and the filepath must be specified for each user due to the size of the files. The data can be accessed here. Click “download all datasets”.
# Set the directory for the data files - must be saved to personal computer (too large for GitHub). Can be accessed here: https://data.cms.gov/provider-data/topics/hospitals
filepath <- "/Users/seugley/Desktop/hospitals_04_2024/"
# List the files in the directory that have "Hospital.csv"
files <- list.files(path = filepath, pattern = "Hospital.csv")
# Iterate through each file in the list
for(f in 1:length(files)) {
# Read the CSV, clean column names to upper camel case, and store in "dat"
dat <- clean_names(read_csv(paste0(filepath, files[f]),
show_col_types = FALSE),
case = "upper_camel")
# Remove ".Hospital.csv" part of the file names to create variable name
filename <- gsub(".Hospital\\.csv", "", files[f])
# Assign data to a variable with the above created name
assign(filename, dat)
}
# Create a df of file names without ".Hospital.csv"
files <- gsub(".Hospital\\.csv", "", files) %>% data.frame()
# Set column name of the df to "File Name"
names(files) <- "File Name"
files %>%
kable(
format = "html",
caption = "Table 1. List of hospital-level data files.") %>%
kable_styling(bootstrap_options = c("striped", full_width = F)
)| File Name |
|---|
| Complications_and_Deaths |
| FY_2024_HAC_Reduction_Program |
| FY_2024_Hospital_Readmissions_Reduction_Program |
| HCAHPS |
| Healthcare_Associated_Infections |
| Maternal_Health |
| Medicare_Hospital_Spending_Per_Patient |
| Outpatient_Imaging_Efficiency |
| Payment_and_Value_of_Care |
| Timely_and_Effective_Care |
| Unplanned_Hospital_Visits |
Exploring and Preprocessing the FY_2024_Hospital_Readmissions_Reduction_Program dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of FY_2024_Hospital_Readmissions_Reduction_Program
head(FY_2024_Hospital_Readmissions_Reduction_Program,10)## # A tibble: 10 × 12
## FacilityName FacilityId State MeasureName NumberOfDischarges Footnote
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH ME… 010001 AL READM-30-H… N/A NA
## 2 SOUTHEAST HEALTH ME… 010001 AL READM-30-H… 616 NA
## 3 SOUTHEAST HEALTH ME… 010001 AL READM-30-A… 274 NA
## 4 SOUTHEAST HEALTH ME… 010001 AL READM-30-P… 404 NA
## 5 SOUTHEAST HEALTH ME… 010001 AL READM-30-C… 126 NA
## 6 SOUTHEAST HEALTH ME… 010001 AL READM-30-C… 117 NA
## 7 MARSHALL MEDICAL CE… 010005 AL READM-30-A… N/A 1
## 8 MARSHALL MEDICAL CE… 010005 AL READM-30-C… 137 NA
## 9 MARSHALL MEDICAL CE… 010005 AL READM-30-P… 285 NA
## 10 MARSHALL MEDICAL CE… 010005 AL READM-30-H… 129 NA
## # ℹ 6 more variables: ExcessReadmissionRatio <chr>,
## # PredictedReadmissionRate <chr>, ExpectedReadmissionRate <chr>,
## # NumberOfReadmissions <chr>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## Footnote
## 12077Replacing values with NA and “Too Few to Report” values with “5”
# Use the function "replace_with_na_all()" to replace aberrant values with NA
FY_2024_Hospital_Readmissions_Reduction_Program <- replace_with_na_all(FY_2024_Hospital_Readmissions_Reduction_Program, condition = ~ .x == "N/A")
# Replace "Too Few to Report" values with "5" in using gsub
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions <- gsub("Too Few to Report", "5", FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions)
# Check first 10 rows to confirm that it worked
head(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions, 10)## [1] "5" "149" "32" "68" "11" "20" NA "14" "40" "24"# NumberOfReadmissions had to be converted to numeric before applying integers
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions <- as.numeric(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions)
# Find all values of "5" in NumberOfReadmissions
fives <- which(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions == 5)
# Replace values of "5" with random integers from 1 - 10
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions[fives] <- sample(1:10, length(fives), replace = TRUE)
# Check the first 20 rows to see if this was applied correctly
head(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions,20)## [1] 3 149 32 68 11 20 NA 14 40 24 3 NA 10 21 15 83 36 75 2
## [20] NAConverting columns to numeric
# Selecting the columns to convert
columns_to_convert <- c("NumberOfDischarges", "ExcessReadmissionRatio", "PredictedReadmissionRate", "ExpectedReadmissionRate", "NumberOfReadmissions")
# Use mutate_at to convert the specified columns to numeric
FY_2024_Hospital_Readmissions_Reduction_Program <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
mutate_at(vars(one_of(columns_to_convert)), as.numeric)
# Print the structure of the dataframe to check the changes
str(FY_2024_Hospital_Readmissions_Reduction_Program)## tibble [18,774 × 12] (S3: tbl_df/tbl/data.frame)
## $ FacilityName : chr [1:18774] "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" ...
## $ FacilityId : chr [1:18774] "010001" "010001" "010001" "010001" ...
## $ State : chr [1:18774] "AL" "AL" "AL" "AL" ...
## $ MeasureName : chr [1:18774] "READM-30-HIP-KNEE-HRRP" "READM-30-HF-HRRP" "READM-30-AMI-HRRP" "READM-30-PN-HRRP" ...
## $ NumberOfDischarges : num [1:18774] NA 616 274 404 126 117 NA 137 285 129 ...
## $ Footnote : num [1:18774] NA NA NA NA NA NA 1 NA NA NA ...
## $ ExcessReadmissionRatio : num [1:18774] 0.892 1.1 0.933 0.987 0.952 ...
## $ PredictedReadmissionRate: num [1:18774] 3.53 23.13 12.9 17.05 9.81 ...
## $ ExpectedReadmissionRate : num [1:18774] 3.96 21.02 13.83 17.28 10.31 ...
## $ NumberOfReadmissions : num [1:18774] 3 149 32 68 11 20 NA 14 40 24 ...
## $ StartDate : chr [1:18774] "07/01/2019" "07/01/2019" "07/01/2019" "07/01/2019" ...
## $ EndDate : chr [1:18774] "06/30/2022" "06/30/2022" "06/30/2022" "06/30/2022" ...Removing excess text from measure names
FY_2024_Hospital_Readmissions_Reduction_Program <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
mutate(MeasureName = gsub("READM-30-", "", MeasureName)) %>%
mutate(MeasureName = gsub("-HRRP", "", MeasureName)) Creating a dictionary for medical conditions
dict <- tribble(
~Acronym, ~Definition,
"HIP-KNEE", "Total Hip/Knee Arthroplasty",
"HF", "Heart Failure",
"COPD", "Chronic Obstructive Pulmonary Disease",
"AMI", "Acute Myocardial Infarction",
"CABG", "Coronary Artery Bypass Graft",
"PN", "Pneumonia"
)
dict %>%
kable(
format = "html",
caption = "Table 2. Acronyms of medical conditions for which hospital readmissions are tracked.") %>%
kable_styling(bootstrap_options = c("hover", full_width = F)
)| Acronym | Definition |
|---|---|
| HIP-KNEE | Total Hip/Knee Arthroplasty |
| HF | Heart Failure |
| COPD | Chronic Obstructive Pulmonary Disease |
| AMI | Acute Myocardial Infarction |
| CABG | Coronary Artery Bypass Graft |
| PN | Pneumonia |
Pivoting the data wider
readmissionsClean <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
pivot_wider(
names_from = MeasureName,
values_from = c(NumberOfDischarges, ExcessReadmissionRatio, PredictedReadmissionRate, ExpectedReadmissionRate, NumberOfReadmissions),
id_cols = c(FacilityName, FacilityId, State, StartDate, EndDate)
)
# Check the new dataframe
dim(readmissionsClean)## [1] 3129 35head(readmissionsClean)## # A tibble: 6 × 35
## FacilityName FacilityId State StartDate EndDate NumberOfDischarges_H…¹
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH ME… 010001 AL 07/01/20… 06/30/… NA
## 2 MARSHALL MEDICAL CE… 010005 AL 07/01/20… 06/30/… NA
## 3 NORTH ALABAMA MEDIC… 010006 AL 07/01/20… 06/30/… NA
## 4 MIZELL MEMORIAL HOS… 010007 AL 07/01/20… 06/30/… NA
## 5 CRENSHAW COMMUNITY … 010008 AL 07/01/20… 06/30/… NA
## 6 ST. VINCENT'S EAST 010011 AL 07/01/20… 06/30/… NA
## # ℹ abbreviated name: ¹`NumberOfDischarges_HIP-KNEE`
## # ℹ 29 more variables: NumberOfDischarges_HF <dbl>,
## # NumberOfDischarges_AMI <dbl>, NumberOfDischarges_PN <dbl>,
## # NumberOfDischarges_CABG <dbl>, NumberOfDischarges_COPD <dbl>,
## # `ExcessReadmissionRatio_HIP-KNEE` <dbl>, ExcessReadmissionRatio_HF <dbl>,
## # ExcessReadmissionRatio_AMI <dbl>, ExcessReadmissionRatio_PN <dbl>,
## # ExcessReadmissionRatio_CABG <dbl>, ExcessReadmissionRatio_COPD <dbl>, …Filtering for only hip/knee conditions
readmissionsClean <- readmissionsClean %>%
select(FacilityName, FacilityId, State, matches("HIP-KNEE$"))Exploring and Preprocessing the HCAHPS dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of HCAHPS
head(HCAHPS,10)## # A tibble: 10 × 22
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 15 more variables: TelephoneNumber <chr>, HcahpsMeasureId <chr>,
## # HcahpsQuestion <chr>, HcahpsAnswerDescription <chr>,
## # PatientSurveyStarRating <chr>, PatientSurveyStarRatingFootnote <dbl>,
## # HcahpsAnswerPercent <chr>, HcahpsAnswerPercentFootnote <chr>,
## # HcahpsLinearMeanValue <chr>, NumberOfCompletedSurveys <chr>,
## # NumberOfCompletedSurveysFootnote <chr>, SurveyResponseRatePercent <chr>,
## # SurveyResponseRatePercentFootnote <chr>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- HCAHPS %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## PatientSurveyStarRatingFootnote
## 430641Removing footnote columns and replacing NA values
# Removing all footnote columns
HCAHPS <- HCAHPS %>%
select(-ends_with("footnote"))
# Replacing all "Not Applicable" with NA
HCAHPS <- as.data.frame(sapply(HCAHPS, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
HCAHPS <- as.data.frame(sapply(HCAHPS, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary for HCAHPS questions
dictHCAHPS <- tribble(
~`Measure ID`, ~`Measure Name`,
"H-CLEAN-HSP-A-P", "Patients who reported that their room and bathroom were 'Always' clean",
"H-CLEAN-HSP-SN-P", "Patients who reported that their room and bathroom were 'Sometimes' or 'Never' clean",
"H-CLEAN-HSP-U-P", "Patients who reported that their room and bathroom were 'Usually' clean",
"H-CLEAN-HSP-STAR-RATING", "Cleanliness - star rating",
"H_CLEAN_LINEAR_SCORE", "Cleanliness - linear mean score",
"H-COMP-1-A-P", "Patients who reported that their nurses 'Always' communicated well",
"H-COMP-1-SN-P", "Patients who reported that their nurses 'Sometimes' or 'Never' communicated well",
"H-COMP-1-U-P", "Patients who reported that their nurses 'Usually' communicated well",
"H-COMP-1-STAR-RATING", "Nurse communication - star rating",
"H_COMP_1_LINEAR_SCORE", "Nurse communication - linear mean score",
"H-COMP-2-A-P", "Patients who reported that their doctors 'Always' communicated well",
"H-COMP-2-SN-P", "Patients who reported that their doctors 'Sometimes' or 'Never' communicated well",
"H-COMP-2-U-P", "Patients who reported that their doctors 'Usually' communicated well",
"H-COMP-2-STAR-RATING", "Doctor communication - star rating",
"H_COMP_2_LINEAR_SCORE", "Doctor communication - linear mean score",
"H-COMP-3-A-P", "Patients who reported that they 'Always' received help as soon as they wanted",
"H-COMP-3-SN-P", "Patients who reported that they 'Sometimes' or 'Never' received help as soon as they wanted",
"H-COMP-3-U-P", "Patients who reported that they 'Usually' received help as soon as they wanted",
"H-COMP-3-STAR-RATING", "Staff responsiveness - star rating",
"H_COMP_3_LINEAR_SCORE", "Staff responsiveness - linear mean score",
"H-COMP-5-A-P", "Patients who reported that staff 'Always' explained about medicines before giving it to them",
"H-COMP-5-SN-P", "Patients who reported that staff 'Sometimes' or 'Never' explained about medicines before giving it to them",
"H-COMP-5-U-P", "Patients who reported that staff 'Usually' explained about medicines before giving it to them",
"H-COMP-5-STAR-RATING", "Communication about medicine - star rating",
"H_COMP_5_LINEAR_SCORE", "Communication about medicines - linear mean score",
"H-COMP-6-N-P", "Patients who reported that NO, they were not given information about what to do during their recovery at home",
"H-COMP-6-Y-P", "Patients who reported that YES, they were given information about what to do during their recovery at home",
"H-COMP-6-STAR-RATING", "Discharge information - star rating",
"H_COMP_6_LINEAR_SCORE", "Discharge information - linear mean score",
"H-COMP-7-A", "Patients who 'Agree' they understood their care when they left the hospital",
"H-COMP-7-D-SD", "Patients who 'Disagree' or 'Strongly Disagree' that they understood their care when they left the hospital",
"H-COMP-7-SA", "Patients who 'Strongly Agree' that they understood their care when they left the hospital",
"H-COMP-7-STAR-RATING", "Care transition - star rating",
"H_COMP_7_LINEAR_SCORE", "Care transition - linear mean score",
"H-HSP-RATING-0-6", "Patients who gave their hospital a rating of 6 or lower on a scale from 0 (lowest) to 10 (highest)",
"H-HSP-RATING-7-8", "Patients who gave their hospital a rating of 7 or 8 on a scale from 0 (lowest) to 10 (highest)",
"H-HSP-RATING-9-10", "Patients who gave their hospital a rating of 9 or 10 on a scale from 0 (lowest) to 10 (highest)",
"H-HSP-RATING-STAR-RATING", "Overall rating of hospital - star rating",
"H_HSP_RATING_LINEAR_SCORE", "Overall hospital rating - linear mean score",
"H-QUIET-HSP-A-P", "Patients who reported that the area around their room was 'Always' quiet at night",
"H-QUIET-HSP-SN-P", "Patients who reported that the area around their room was 'Sometimes' or 'Never' quiet at night",
"H-QUIET-HSP-U-P", "Patients who reported that the area around their room was 'Usually' quiet at night",
"H-QUIET-HSP-STAR-RATING", "Quietness - star rating",
"H_QUIET_LINEAR_SCORE", "Quietness - linear mean score",
"H-RECMND-DN", "Patients who reported NO, they would probably not or definitely not recommend the hospital",
"H-RECMND-DY", "Patients who reported YES, they would definitely recommend the hospital",
"H-RECMND-PY", "Patients who reported YES, they would probably recommend the hospital",
"H-RECMND-STAR-RATING", "Recommend hospital - star rating",
"H_RECMND_LINEAR_SCORE", "Recommend hospital - linear mean score",
"H-STAR-RATING", "Summary star rating"
)
dictHCAHPS %>%
kable(
format = "html",
caption = "Table 3. Measure IDs and Measure Names from HCAHPS") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| H-CLEAN-HSP-A-P | Patients who reported that their room and bathroom were ‘Always’ clean |
| H-CLEAN-HSP-SN-P | Patients who reported that their room and bathroom were ‘Sometimes’ or ‘Never’ clean |
| H-CLEAN-HSP-U-P | Patients who reported that their room and bathroom were ‘Usually’ clean |
| H-CLEAN-HSP-STAR-RATING | Cleanliness - star rating |
| H_CLEAN_LINEAR_SCORE | Cleanliness - linear mean score |
| H-COMP-1-A-P | Patients who reported that their nurses ‘Always’ communicated well |
| H-COMP-1-SN-P | Patients who reported that their nurses ‘Sometimes’ or ‘Never’ communicated well |
| H-COMP-1-U-P | Patients who reported that their nurses ‘Usually’ communicated well |
| H-COMP-1-STAR-RATING | Nurse communication - star rating |
| H_COMP_1_LINEAR_SCORE | Nurse communication - linear mean score |
| H-COMP-2-A-P | Patients who reported that their doctors ‘Always’ communicated well |
| H-COMP-2-SN-P | Patients who reported that their doctors ‘Sometimes’ or ‘Never’ communicated well |
| H-COMP-2-U-P | Patients who reported that their doctors ‘Usually’ communicated well |
| H-COMP-2-STAR-RATING | Doctor communication - star rating |
| H_COMP_2_LINEAR_SCORE | Doctor communication - linear mean score |
| H-COMP-3-A-P | Patients who reported that they ‘Always’ received help as soon as they wanted |
| H-COMP-3-SN-P | Patients who reported that they ‘Sometimes’ or ‘Never’ received help as soon as they wanted |
| H-COMP-3-U-P | Patients who reported that they ‘Usually’ received help as soon as they wanted |
| H-COMP-3-STAR-RATING | Staff responsiveness - star rating |
| H_COMP_3_LINEAR_SCORE | Staff responsiveness - linear mean score |
| H-COMP-5-A-P | Patients who reported that staff ‘Always’ explained about medicines before giving it to them |
| H-COMP-5-SN-P | Patients who reported that staff ‘Sometimes’ or ‘Never’ explained about medicines before giving it to them |
| H-COMP-5-U-P | Patients who reported that staff ‘Usually’ explained about medicines before giving it to them |
| H-COMP-5-STAR-RATING | Communication about medicine - star rating |
| H_COMP_5_LINEAR_SCORE | Communication about medicines - linear mean score |
| H-COMP-6-N-P | Patients who reported that NO, they were not given information about what to do during their recovery at home |
| H-COMP-6-Y-P | Patients who reported that YES, they were given information about what to do during their recovery at home |
| H-COMP-6-STAR-RATING | Discharge information - star rating |
| H_COMP_6_LINEAR_SCORE | Discharge information - linear mean score |
| H-COMP-7-A | Patients who ‘Agree’ they understood their care when they left the hospital |
| H-COMP-7-D-SD | Patients who ‘Disagree’ or ‘Strongly Disagree’ that they understood their care when they left the hospital |
| H-COMP-7-SA | Patients who ‘Strongly Agree’ that they understood their care when they left the hospital |
| H-COMP-7-STAR-RATING | Care transition - star rating |
| H_COMP_7_LINEAR_SCORE | Care transition - linear mean score |
| H-HSP-RATING-0-6 | Patients who gave their hospital a rating of 6 or lower on a scale from 0 (lowest) to 10 (highest) |
| H-HSP-RATING-7-8 | Patients who gave their hospital a rating of 7 or 8 on a scale from 0 (lowest) to 10 (highest) |
| H-HSP-RATING-9-10 | Patients who gave their hospital a rating of 9 or 10 on a scale from 0 (lowest) to 10 (highest) |
| H-HSP-RATING-STAR-RATING | Overall rating of hospital - star rating |
| H_HSP_RATING_LINEAR_SCORE | Overall hospital rating - linear mean score |
| H-QUIET-HSP-A-P | Patients who reported that the area around their room was ‘Always’ quiet at night |
| H-QUIET-HSP-SN-P | Patients who reported that the area around their room was ‘Sometimes’ or ‘Never’ quiet at night |
| H-QUIET-HSP-U-P | Patients who reported that the area around their room was ‘Usually’ quiet at night |
| H-QUIET-HSP-STAR-RATING | Quietness - star rating |
| H_QUIET_LINEAR_SCORE | Quietness - linear mean score |
| H-RECMND-DN | Patients who reported NO, they would probably not or definitely not recommend the hospital |
| H-RECMND-DY | Patients who reported YES, they would definitely recommend the hospital |
| H-RECMND-PY | Patients who reported YES, they would probably recommend the hospital |
| H-RECMND-STAR-RATING | Recommend hospital - star rating |
| H_RECMND_LINEAR_SCORE | Recommend hospital - linear mean score |
| H-STAR-RATING | Summary star rating |
Pivoting the data wider
HCAHPSClean <- HCAHPS %>%
pivot_wider(
names_from = HcahpsMeasureId,
values_from = c(PatientSurveyStarRating, HcahpsAnswerPercent, HcahpsLinearMeanValue, SurveyResponseRatePercent),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(HCAHPSClean)## [1] 4814 375head(HCAHPSClean)## # A tibble: 6 × 375
## FacilityName FacilityId State PatientSurveyStarRat…¹ PatientSurveyStarRat…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL <NA> <NA>
## 2 MARSHALL MEDIC… 010005 AL <NA> <NA>
## 3 NORTH ALABAMA … 010006 AL <NA> <NA>
## 4 MIZELL MEMORIA… 010007 AL <NA> <NA>
## 5 CRENSHAW COMMU… 010008 AL <NA> <NA>
## 6 ST. VINCENT'S … 010011 AL <NA> <NA>
## # ℹ abbreviated names: ¹PatientSurveyStarRating_H_COMP_1_A_P,
## # ²PatientSurveyStarRating_H_COMP_1_SN_P
## # ℹ 370 more variables: PatientSurveyStarRating_H_COMP_1_U_P <chr>,
## # PatientSurveyStarRating_H_COMP_1_LINEAR_SCORE <chr>,
## # PatientSurveyStarRating_H_COMP_1_STAR_RATING <chr>,
## # PatientSurveyStarRating_H_NURSE_RESPECT_A_P <chr>,
## # PatientSurveyStarRating_H_NURSE_RESPECT_SN_P <chr>, …Exploring and Preprocessing the Timely_and_Effective_Care dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of Timely_and_Effective_Care
head(Timely_and_Effective_Care,10)## # A tibble: 10 × 16
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 9 more variables: TelephoneNumber <chr>, Condition <chr>, MeasureId <chr>,
## # MeasureName <chr>, Score <chr>, Sample <chr>, Footnote <chr>,
## # StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Timely_and_Effective_Care %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## named list()Replacing NA values
# Replacing all "Not Applicable" with NA
Timely_and_Effective_Care <- as.data.frame(sapply(Timely_and_Effective_Care, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Timely_and_Effective_Care <- as.data.frame(sapply(Timely_and_Effective_Care, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary Timely and Effective Care measure names
dictCare <- tribble(
~`Measure ID`, ~`Measure Name`,
"EDV", "Emergency department volume (alternate Measure ID: EDV-1)",
"ED-2", "Average (median) admit decision time to time of departure from the emergency department for emergency department patients admitted to inpatient status",
"IMM-3", "Healthcare workers given influenza vaccination",
"HCP COVID-19", "COVID-19 Vaccination Coverage Among HCP",
"OP-18b", "Average (median) time patients spent in the emergency department before leaving from the visit (alternate Measure ID: OP-18)",
"OP-18c", "Average time patients spent in the emergency department before being sent home (Median Time from ED Arrival to ED Departure for Discharged ED Patients – Psychiatric/Mental Health Patients) *This measure is only found in the downloadable database, it is not displayed on Hospital Care Compare",
"OP-22", "Percentage of patients who left the emergency department before being seen",
"OP-23", "Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival",
"OP-29", "Percentage of patients receiving appropriate recommendation for follow-up screening colonoscopy",
"OP-31", "Percentage of patients who had cataract surgery and had improvement in visual function within 90 days following the surgery",
"SEP-1", "Severe Sepsis and Septic Shock",
"SEP-SH-3HR", "Septic Shock 3 Hour",
"SEP-SH-6HR", "Septic Shock 6 Hour",
"SEV-SEP-3HR", "Severe Sepsis 3 Hour",
"SEV-SEP-6HR", "Severe Sepsis 6 Hour",
"STK-02", "Percentage of ischemic stroke patients prescribed or continuing to take antithrombotic therapy at hospital discharge",
"STK-03", "Percentage of ischemic stroke patients with atrial fibrillation/flutter who are prescribed or continuing to take anticoagulation therapy at hospital discharge",
"STK-05", "Percentage of ischemic stroke patients administered antithrombotic therapy by the end of hospital day 2",
"STK-06", "Percentage of ischemic stroke patients who are prescribed or continuing to take statin medication at hospital discharge",
"VTE-1", "Percentage of patients that received VTE prophylaxis after hospital admission or surgery",
"VTE-2", "Percentage of patients that received VTE prophylaxis after being admitted to the intensive care unit (ICU)",
"Safe Use of Opioids", "Percentage of patients who were prescribed 2 or more opioids or an opioid and benzodiazepine concurrently at discharge"
)
dictCare %>%
kable(
format = "html",
caption = "Table 4. Measure IDs and Measure Names from Timely and Effective Care") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| EDV | Emergency department volume (alternate Measure ID: EDV-1) |
| ED-2 | Average (median) admit decision time to time of departure from the emergency department for emergency department patients admitted to inpatient status |
| IMM-3 | Healthcare workers given influenza vaccination |
| HCP COVID-19 | COVID-19 Vaccination Coverage Among HCP |
| OP-18b | Average (median) time patients spent in the emergency department before leaving from the visit (alternate Measure ID: OP-18) |
| OP-18c | Average time patients spent in the emergency department before being sent home (Median Time from ED Arrival to ED Departure for Discharged ED Patients – Psychiatric/Mental Health Patients) *This measure is only found in the downloadable database, it is not displayed on Hospital Care Compare |
| OP-22 | Percentage of patients who left the emergency department before being seen |
| OP-23 | Percentage of patients who came to the emergency department with stroke symptoms who received brain scan results within 45 minutes of arrival |
| OP-29 | Percentage of patients receiving appropriate recommendation for follow-up screening colonoscopy |
| OP-31 | Percentage of patients who had cataract surgery and had improvement in visual function within 90 days following the surgery |
| SEP-1 | Severe Sepsis and Septic Shock |
| SEP-SH-3HR | Septic Shock 3 Hour |
| SEP-SH-6HR | Septic Shock 6 Hour |
| SEV-SEP-3HR | Severe Sepsis 3 Hour |
| SEV-SEP-6HR | Severe Sepsis 6 Hour |
| STK-02 | Percentage of ischemic stroke patients prescribed or continuing to take antithrombotic therapy at hospital discharge |
| STK-03 | Percentage of ischemic stroke patients with atrial fibrillation/flutter who are prescribed or continuing to take anticoagulation therapy at hospital discharge |
| STK-05 | Percentage of ischemic stroke patients administered antithrombotic therapy by the end of hospital day 2 |
| STK-06 | Percentage of ischemic stroke patients who are prescribed or continuing to take statin medication at hospital discharge |
| VTE-1 | Percentage of patients that received VTE prophylaxis after hospital admission or surgery |
| VTE-2 | Percentage of patients that received VTE prophylaxis after being admitted to the intensive care unit (ICU) |
| Safe Use of Opioids | Percentage of patients who were prescribed 2 or more opioids or an opioid and benzodiazepine concurrently at discharge |
Pivoting the data wider
careClean <- Timely_and_Effective_Care %>%
pivot_wider(
names_from = MeasureId,
values_from = c(Score),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(careClean)## [1] 4677 26head(careClean)## # A tibble: 6 × 26
## FacilityName FacilityId State EDV ED_2_Strata_1 ED_2_Strata_2 HCP_COVID_19
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEA… 010001 AL high <NA> <NA> 80.7
## 2 MARSHALL MEDI… 010005 AL high 148 105 79.8
## 3 NORTH ALABAMA… 010006 AL high <NA> <NA> 79
## 4 MIZELL MEMORI… 010007 AL low <NA> <NA> 57.9
## 5 CRENSHAW COMM… 010008 AL low <NA> <NA> 81.2
## 6 ST. VINCENT'S… 010011 AL high <NA> <NA> 88
## # ℹ 19 more variables: IMM_3 <chr>, OP_18b <chr>, OP_18c <chr>, OP_22 <chr>,
## # OP_23 <chr>, OP_29 <chr>, OP_31 <chr>, SAFE_USE_OF_OPIOIDS <chr>,
## # SEP_1 <chr>, SEP_SH_3HR <chr>, SEP_SH_6HR <chr>, SEV_SEP_3HR <chr>,
## # SEV_SEP_6HR <chr>, STK_02 <chr>, STK_03 <chr>, STK_05 <chr>, STK_06 <chr>,
## # VTE_1 <chr>, VTE_2 <chr>Exploring and Preprocessing the Complications_and_Deaths dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of Complications_and_Deaths
head(Complications_and_Deaths,10)## # A tibble: 10 × 18
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 11 more variables: TelephoneNumber <chr>, MeasureId <chr>,
## # MeasureName <chr>, ComparedToNational <chr>, Denominator <chr>,
## # Score <chr>, LowerEstimate <chr>, HigherEstimate <chr>, Footnote <chr>,
## # StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Complications_and_Deaths %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## named list()Replacing NA values
# Replacing all "Not Applicable" with NA
Complications_and_Deaths <- as.data.frame(sapply(Complications_and_Deaths, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Complications_and_Deaths <- as.data.frame(sapply(Complications_and_Deaths, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary for Complications and Deaths measure names
dictDeaths <- tribble(
~`Measure ID`, ~`Measure Name`,
"COMP-HIP-KNEE", "Rate of complications for hip/knee replacement patients",
"PSI 90", "Serious complications (this is a composite or summary measure; alternate Measure ID: PSI-90-SAFETY)",
"PSI 03", "Pressure sores (alternate Measure ID: PSI_3_Ulcer)",
"PSI 04", "Deaths among patients with serious treatable complications after surgery (alternate Measure ID: PSI-4-SURG-COMP)",
"PSI 06", "Collapsed lung due to medical treatment (alternate Measure ID: PSI-6-IAT-PTX)",
"PSI 08", "Broken hip from a fall after surgery (alternate Measure ID: PSI_8_POST_HIP)",
"PSI 09", "Postoperative hemorrhage or hematoma rate (alternate Measure ID: PSI_9_POST_HEM)",
"PSI 10", "Kidney and diabetic complications after surgery (alternate Measure ID: PSI_10_POST_KIDNEY)",
"PSI 11", "Respiratory failure after surgery (alternate Measure ID: PSI_11_POST_RESP)",
"PSI 12", "Serious blood clots after surgery (alternate Measure ID: PSI-12-POSTOP-PULMEMB-DVT)",
"PSI 13", "Blood stream infection after surgery (alternate Measure ID: PSI_13_POST_SEPSIS)",
"PSI 14", "A wound that splits open after surgery on the abdomen or pelvis (alternate Measure ID: PSI-14-POSTOP-DEHIS)",
"PSI 15", "Accidental cuts and tears from medical treatment (alternate Measure ID: PSI-15-ACC-LAC)",
"MORT-30-AMI", "Death rate for heart attack patients",
"MORT-30-CABG", "Death rate for Coronary Artery Bypass Graft (CABG) surgery patients",
"MORT-30-COPD", "Death rate for chronic obstructive pulmonary disease (COPD) patients",
"MORT-30-HF", "Death rate for heart failure patients",
"MORT-30-PN", "Death rate for pneumonia patients",
"MORT-30-STK", "Death rate for stroke patients"
)
dictDeaths %>%
kable(
format = "html",
caption = "Table 5. Measure IDs and Measure Names from Complications and Deaths") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| COMP-HIP-KNEE | Rate of complications for hip/knee replacement patients |
| PSI 90 | Serious complications (this is a composite or summary measure; alternate Measure ID: PSI-90-SAFETY) |
| PSI 03 | Pressure sores (alternate Measure ID: PSI_3_Ulcer) |
| PSI 04 | Deaths among patients with serious treatable complications after surgery (alternate Measure ID: PSI-4-SURG-COMP) |
| PSI 06 | Collapsed lung due to medical treatment (alternate Measure ID: PSI-6-IAT-PTX) |
| PSI 08 | Broken hip from a fall after surgery (alternate Measure ID: PSI_8_POST_HIP) |
| PSI 09 | Postoperative hemorrhage or hematoma rate (alternate Measure ID: PSI_9_POST_HEM) |
| PSI 10 | Kidney and diabetic complications after surgery (alternate Measure ID: PSI_10_POST_KIDNEY) |
| PSI 11 | Respiratory failure after surgery (alternate Measure ID: PSI_11_POST_RESP) |
| PSI 12 | Serious blood clots after surgery (alternate Measure ID: PSI-12-POSTOP-PULMEMB-DVT) |
| PSI 13 | Blood stream infection after surgery (alternate Measure ID: PSI_13_POST_SEPSIS) |
| PSI 14 | A wound that splits open after surgery on the abdomen or pelvis (alternate Measure ID: PSI-14-POSTOP-DEHIS) |
| PSI 15 | Accidental cuts and tears from medical treatment (alternate Measure ID: PSI-15-ACC-LAC) |
| MORT-30-AMI | Death rate for heart attack patients |
| MORT-30-CABG | Death rate for Coronary Artery Bypass Graft (CABG) surgery patients |
| MORT-30-COPD | Death rate for chronic obstructive pulmonary disease (COPD) patients |
| MORT-30-HF | Death rate for heart failure patients |
| MORT-30-PN | Death rate for pneumonia patients |
| MORT-30-STK | Death rate for stroke patients |
Pivoting the data wider
deathsClean <- Complications_and_Deaths %>%
pivot_wider(
names_from = MeasureId,
values_from = c(ComparedToNational, Score),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(deathsClean)## [1] 4814 41head(deathsClean)## # A tibble: 6 × 41
## FacilityName FacilityId State ComparedToNational_C…¹ ComparedToNational_M…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL No Different Than the… No Different Than the…
## 2 MARSHALL MEDIC… 010005 AL No Different Than the… No Different Than the…
## 3 NORTH ALABAMA … 010006 AL No Different Than the… Worse Than the Nation…
## 4 MIZELL MEMORIA… 010007 AL Number of Cases Too S… Number of Cases Too S…
## 5 CRENSHAW COMMU… 010008 AL <NA> Number of Cases Too S…
## 6 ST. VINCENT'S … 010011 AL No Different Than the… No Different Than the…
## # ℹ abbreviated names: ¹ComparedToNational_COMP_HIP_KNEE,
## # ²ComparedToNational_MORT_30_AMI
## # ℹ 36 more variables: ComparedToNational_MORT_30_CABG <chr>,
## # ComparedToNational_MORT_30_COPD <chr>, ComparedToNational_MORT_30_HF <chr>,
## # ComparedToNational_MORT_30_PN <chr>, ComparedToNational_MORT_30_STK <chr>,
## # ComparedToNational_PSI_03 <chr>, ComparedToNational_PSI_04 <chr>,
## # ComparedToNational_PSI_06 <chr>, ComparedToNational_PSI_08 <chr>, …Exploring and Preprocessing the Payment_and_Value_of_Care dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of Payment_and_Value_of_Care
head(Payment_and_Value_of_Care,10)## # A tibble: 10 × 22
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 6 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 7 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 8 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 9 010006 NORTH ALABAMA MEDICAL… 1701 V… FLORENCE AL 35630 LAUDERDALE
## 10 010006 NORTH ALABAMA MEDICAL… 1701 V… FLORENCE AL 35630 LAUDERDALE
## # ℹ 15 more variables: TelephoneNumber <chr>, PaymentMeasureId <chr>,
## # PaymentMeasureName <chr>, PaymentCategory <chr>, Denominator <chr>,
## # Payment <chr>, LowerEstimate <chr>, HigherEstimate <chr>,
## # PaymentFootnote <dbl>, ValueOfCareDisplayId <chr>,
## # ValueOfCareDisplayName <chr>, ValueOfCareCategory <chr>,
## # ValueOfCareFootnote <dbl>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Payment_and_Value_of_Care %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## PaymentFootnote ValueOfCareFootnote
## 9956 10044Replacing NA values
# Replacing all "Not Applicable" with NA
Payment_and_Value_of_Care <- as.data.frame(sapply(Payment_and_Value_of_Care, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Payment_and_Value_of_Care <- as.data.frame(sapply(Payment_and_Value_of_Care, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Creating a dictionary for Payment and Value of Care measure names
dictPayment <- tribble(
~`Measure ID`, ~`Measure Name`,
"PAYM-30-AMI", "Payment for heart attack patients",
"PAYM-30-HF", "Payment for heart failure patients",
"PAYM-30-PN", "Payment for pneumonia patients",
"PAYM_90_HIP_KNEE", "Payment for hip/knee replacement patients"
)
dictPayment %>%
kable(
format = "html",
caption = "Table 6. Measure IDs and Measure Names from Payment and Value of Care") %>%
kable_styling(bootstrap_options = c("hover", "full_width" = F))| Measure ID | Measure Name |
|---|---|
| PAYM-30-AMI | Payment for heart attack patients |
| PAYM-30-HF | Payment for heart failure patients |
| PAYM-30-PN | Payment for pneumonia patients |
| PAYM_90_HIP_KNEE | Payment for hip/knee replacement patients |
Pivoting the data wider
paymentClean <- Payment_and_Value_of_Care %>%
pivot_wider(
names_from = PaymentMeasureId,
values_from = c(PaymentCategory, Payment),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(paymentClean)## [1] 4645 11head(paymentClean)## # A tibble: 6 × 11
## FacilityName FacilityId State PaymentCategory_PAYM…¹ PaymentCategory_PAYM…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL No Different Than the… No Different Than the…
## 2 MARSHALL MEDIC… 010005 AL No Different Than the… No Different Than the…
## 3 NORTH ALABAMA … 010006 AL Greater Than the Nati… No Different Than the…
## 4 MIZELL MEMORIA… 010007 AL Number of Cases Too S… No Different Than the…
## 5 CRENSHAW COMMU… 010008 AL Number of Cases Too S… Number of Cases Too S…
## 6 ST. VINCENT'S … 010011 AL No Different Than the… No Different Than the…
## # ℹ abbreviated names: ¹PaymentCategory_PAYM_30_AMI,
## # ²PaymentCategory_PAYM_30_HF
## # ℹ 6 more variables: PaymentCategory_PAYM_30_PN <chr>,
## # PaymentCategory_PAYM_90_HIP_KNEE <chr>, Payment_PAYM_30_AMI <chr>,
## # Payment_PAYM_30_HF <chr>, Payment_PAYM_30_PN <chr>,
## # Payment_PAYM_90_HIP_KNEE <chr>Joining and cleaning the datasets (AC)
Joining the datasets based on FacilityId
HipKneeClean <- readmissionsClean %>%
full_join(HCAHPSClean, by = "FacilityId") %>%
full_join(careClean, by = "FacilityId") %>%
full_join(deathsClean, by = "FacilityId") %>%
full_join(paymentClean, by = "FacilityId")
head(HipKneeClean)## # A tibble: 6 × 451
## FacilityName.x FacilityId State.x NumberOfDischarges_HIP-KN…¹
## <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH MEDICAL CENTER 010001 AL NA
## 2 MARSHALL MEDICAL CENTERS 010005 AL NA
## 3 NORTH ALABAMA MEDICAL CENTER 010006 AL NA
## 4 MIZELL MEMORIAL HOSPITAL 010007 AL NA
## 5 CRENSHAW COMMUNITY HOSPITAL 010008 AL NA
## 6 ST. VINCENT'S EAST 010011 AL NA
## # ℹ abbreviated name: ¹`NumberOfDischarges_HIP-KNEE`
## # ℹ 447 more variables: `ExcessReadmissionRatio_HIP-KNEE` <dbl>,
## # `PredictedReadmissionRate_HIP-KNEE` <dbl>,
## # `ExpectedReadmissionRate_HIP-KNEE` <dbl>,
## # `NumberOfReadmissions_HIP-KNEE` <dbl>, FacilityName.y <chr>, State.y <chr>,
## # PatientSurveyStarRating_H_COMP_1_A_P <chr>,
## # PatientSurveyStarRating_H_COMP_1_SN_P <chr>, …Removing redundant columns
# Removing duplicate columns
HipKneeClean <- HipKneeClean %>%
select(-matches("\\.(x|y|z|w|v)$"))Checking for NA Values
# Checking the dimensions
dim(HipKneeClean)
# Count NA values in each column
na_counts <- sapply(HipKneeClean, function(x) sum(is.na(x)))
# View the NA counts
print(na_counts)Removing columns with more than 80% NA values
# Calculate the percentage of NA values for each column
na_percentage <- sapply(HipKneeClean, function(x) mean(is.na(x)))
# Remove columns where more than 80% of the values are NA
HipKneeClean <- HipKneeClean[, na_percentage <= 0.8]
# Count NA values in each column
na_counts <- sapply(HipKneeClean, function(x) sum(is.na(x)))
# View the NA counts
print(na_counts)
# Check the dimensions
dim(HipKneeClean)Removing answer percent and survey response percent columns
# Remove columns containing 'AnswerPercent' or 'SurveyResponseRate'
HipKneeClean <- HipKneeClean %>%
select(-matches("AnswerPercent|SurveyResponseRate"))
# Check the dimensions
dim(HipKneeClean)## [1] 4816 87Removing compared to national columns
# Remove columns containing 'ComparedToNational' and 'PaymentCategory'
HipKneeClean <- HipKneeClean %>%
select(-matches("ComparedToNational|PaymentCategory"))
# Check the dimensions
dim(HipKneeClean)## [1] 4816 67Checking data structure
str(HipKneeClean)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 3 3 10 5 NA 6 10 9 NA 9 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : chr [1:4816] "3" "3" "2" "3" ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : chr [1:4816] "4" "4" "3" "5" ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : chr [1:4816] "3" "2" "2" "4" ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : chr [1:4816] "3" "3" "2" "3" ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : chr [1:4816] "4" "3" "3" "4" ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : chr [1:4816] "3" "2" "1" "2" ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : chr [1:4816] "4" "4" "4" "4" ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : chr [1:4816] "89" "90" "88" "91" ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : chr [1:4816] "91" "92" "89" "95" ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : chr [1:4816] "81" "75" "75" "88" ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : chr [1:4816] "77" "76" "71" "77" ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : chr [1:4816] "87" "86" "83" "87" ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : chr [1:4816] "82" "79" "77" "82" ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : chr [1:4816] "84" "80" "74" "80" ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : chr [1:4816] "86" "85" "85" "87" ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : chr [1:4816] "89" "85" "82" "89" ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : chr [1:4816] "90" "83" "79" "88" ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : chr [1:4816] NA "148" NA NA ...
## $ HCP_COVID_19 : chr [1:4816] "80.7" "79.8" "79" "57.9" ...
## $ IMM_3 : chr [1:4816] "95" "80" "67" "53" ...
## $ OP_18b : chr [1:4816] "215" "147" "177" "130" ...
## $ OP_18c : chr [1:4816] "317" "266" NA "216" ...
## $ OP_22 : chr [1:4816] "5" "3" "1" "4" ...
## $ OP_23 : chr [1:4816] NA NA "69" NA ...
## $ OP_29 : chr [1:4816] "47" "96" "85" "23" ...
## $ SAFE_USE_OF_OPIOIDS : chr [1:4816] "14" "19" "17" NA ...
## $ SEP_1 : chr [1:4816] "66" "74" "56" "86" ...
## $ SEP_SH_3HR : chr [1:4816] "70" "88" "77" NA ...
## $ SEP_SH_6HR : chr [1:4816] "100" "91" "81" NA ...
## $ SEV_SEP_3HR : chr [1:4816] "79" "88" "78" "89" ...
## $ SEV_SEP_6HR : chr [1:4816] "95" "96" "86" "97" ...
## $ STK_02 : chr [1:4816] "98" "100" "96" NA ...
## $ STK_05 : chr [1:4816] NA "91" NA NA ...
## $ STK_06 : chr [1:4816] NA NA "97" NA ...
## $ VTE_1 : chr [1:4816] "98" NA NA NA ...
## $ VTE_2 : chr [1:4816] "99" NA "97" NA ...
## $ Score_COMP_HIP_KNEE : chr [1:4816] "2.7" "2.3" "4.6" NA ...
## $ Score_MORT_30_AMI : chr [1:4816] "12" "13.6" "16.5" NA ...
## $ Score_MORT_30_COPD : chr [1:4816] "8.8" "9.9" "9.9" "13.7" ...
## $ Score_MORT_30_HF : chr [1:4816] "8.9" "14.9" "12.5" "12.5" ...
## $ Score_MORT_30_PN : chr [1:4816] "18" "23.3" "19.5" "28.5" ...
## $ Score_MORT_30_STK : chr [1:4816] "14.8" "15.3" "17.2" NA ...
## $ Score_PSI_03 : chr [1:4816] "0.39" "0.94" "1.39" "0.42" ...
## $ Score_PSI_04 : chr [1:4816] "184.68" "183.49" "173.63" NA ...
## $ Score_PSI_06 : chr [1:4816] "0.23" "0.22" "0.36" "0.24" ...
## $ Score_PSI_08 : chr [1:4816] "0.10" "0.09" "0.08" "0.09" ...
## $ Score_PSI_09 : chr [1:4816] "2.39" "2.69" "5.43" "2.49" ...
## $ Score_PSI_10 : chr [1:4816] "1.14" "1.37" "1.26" "1.57" ...
## $ Score_PSI_11 : chr [1:4816] "13.83" "7.19" "7.37" "8.45" ...
## $ Score_PSI_12 : chr [1:4816] "4.49" "3.01" "3.36" "3.89" ...
## $ Score_PSI_13 : chr [1:4816] "8.05" "4.46" "4.37" "5.19" ...
## $ Score_PSI_14 : chr [1:4816] "1.69" "1.87" "1.76" NA ...
## $ Score_PSI_15 : chr [1:4816] "0.93" "0.91" "1.34" "1.08" ...
## $ Score_PSI_90 : chr [1:4816] "1.21" "0.97" "1.17" "0.95" ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : chr [1:4816] "$22,212" "$18,030" "$21,898" NA ...# Convert columns to numeric
HipKneeClean <- HipKneeClean %>%
mutate_at(vars(starts_with("PatientSurveyStarRating_"),
starts_with("HcahpsLinearMeanValue_"),
starts_with("Score_"),
starts_with("ED_"),
starts_with("IMM_"),
starts_with("OP_"),
starts_with("SEP_"),
starts_with("SEV_"),
starts_with("STK_"),
starts_with("VTE_"),
starts_with("SAFE_"),
starts_with("HCP_")),
~ as.numeric(as.character(.)))
# View the structure
str(HipKneeClean)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 3 3 10 5 NA 6 10 9 NA 9 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : num [1:4816] 4 4 3 5 NA 3 4 4 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : num [1:4816] 3 2 2 4 NA 4 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : num [1:4816] 4 3 3 4 NA 3 3 2 NA 3 ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : num [1:4816] 3 2 1 2 NA 2 2 1 NA 4 ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : num [1:4816] 4 4 4 4 NA 4 4 3 NA 5 ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: num [1:4816] 4 3 2 4 NA 3 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : num [1:4816] 4 3 2 4 NA 4 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : num [1:4816] 89 90 88 91 NA 90 91 89 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : num [1:4816] 91 92 89 95 NA 90 91 91 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : num [1:4816] 81 75 75 88 NA 85 80 78 NA 85 ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : num [1:4816] 77 76 71 77 NA 76 76 72 NA 78 ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : num [1:4816] 87 86 83 87 NA 86 86 81 NA 86 ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : num [1:4816] 82 79 77 82 NA 81 79 80 NA 83 ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : num [1:4816] 84 80 74 80 NA 81 83 78 NA 88 ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : num [1:4816] 86 85 85 87 NA 84 84 82 NA 89 ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : num [1:4816] 89 85 82 89 NA 88 83 85 NA 90 ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : num [1:4816] 90 83 79 88 NA 87 80 84 NA 91 ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : num [1:4816] NA 148 NA NA NA NA NA NA NA NA ...
## $ HCP_COVID_19 : num [1:4816] 80.7 79.8 79 57.9 81.2 88 69.8 87.3 95.9 85.3 ...
## $ IMM_3 : num [1:4816] 95 80 67 53 45 81 65 93 98 81 ...
## $ OP_18b : num [1:4816] 215 147 177 130 118 206 160 185 102 145 ...
## $ OP_18c : num [1:4816] 317 266 NA 216 98 124 220 220 NA 324 ...
## $ OP_22 : num [1:4816] 5 3 1 4 0 5 4 3 0 2 ...
## $ OP_23 : num [1:4816] NA NA 69 NA NA 47 NA 73 NA 35 ...
## $ OP_29 : num [1:4816] 47 96 85 23 67 100 100 NA NA 82 ...
## $ SAFE_USE_OF_OPIOIDS : num [1:4816] 14 19 17 NA NA 20 14 23 NA 17 ...
## $ SEP_1 : num [1:4816] 66 74 56 86 NA 51 92 77 NA 87 ...
## $ SEP_SH_3HR : num [1:4816] 70 88 77 NA NA 78 94 83 NA 90 ...
## $ SEP_SH_6HR : num [1:4816] 100 91 81 NA NA 81 83 100 NA 94 ...
## $ SEV_SEP_3HR : num [1:4816] 79 88 78 89 NA 69 95 85 NA 94 ...
## $ SEV_SEP_6HR : num [1:4816] 95 96 86 97 NA 91 99 97 NA 99 ...
## $ STK_02 : num [1:4816] 98 100 96 NA NA 93 NA 99 NA NA ...
## $ STK_05 : num [1:4816] NA 91 NA NA NA NA NA NA NA NA ...
## $ STK_06 : num [1:4816] NA NA 97 NA NA NA NA NA NA NA ...
## $ VTE_1 : num [1:4816] 98 NA NA NA NA 79 89 84 44 59 ...
## $ VTE_2 : num [1:4816] 99 NA 97 NA NA 88 93 94 NA NA ...
## $ Score_COMP_HIP_KNEE : num [1:4816] 2.7 2.3 4.6 NA NA 3.5 3.8 3.5 NA 4.3 ...
## $ Score_MORT_30_AMI : num [1:4816] 12 13.6 16.5 NA NA 13.2 13.8 13.1 NA NA ...
## $ Score_MORT_30_COPD : num [1:4816] 8.8 9.9 9.9 13.7 NA 10.3 NA 9.2 NA 7.8 ...
## $ Score_MORT_30_HF : num [1:4816] 8.9 14.9 12.5 12.5 NA 13.5 13.6 9.9 NA 16.9 ...
## $ Score_MORT_30_PN : num [1:4816] 18 23.3 19.5 28.5 NA 20.9 22 17.2 NA 26.1 ...
## $ Score_MORT_30_STK : num [1:4816] 14.8 15.3 17.2 NA NA 12.3 NA 13.2 NA 17.3 ...
## $ Score_PSI_03 : num [1:4816] 0.39 0.94 1.39 0.42 0.54 0.13 0.41 0.63 0.57 0.47 ...
## $ Score_PSI_04 : num [1:4816] 185 183 174 NA NA ...
## $ Score_PSI_06 : num [1:4816] 0.23 0.22 0.36 0.24 0.25 0.24 0.24 0.21 0.25 0.22 ...
## $ Score_PSI_08 : num [1:4816] 0.1 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 ...
## $ Score_PSI_09 : num [1:4816] 2.39 2.69 5.43 2.49 NA 1.88 2.44 3.29 2.44 2.58 ...
## $ Score_PSI_10 : num [1:4816] 1.14 1.37 1.26 1.57 NA 1.72 1.51 1.2 1.57 NA ...
## $ Score_PSI_11 : num [1:4816] 13.83 7.19 7.37 8.45 NA ...
## $ Score_PSI_12 : num [1:4816] 4.49 3.01 3.36 3.89 NA 3.04 3.32 3.67 3.56 5.63 ...
## $ Score_PSI_13 : num [1:4816] 8.05 4.46 4.37 5.19 NA 5.55 4.88 6.08 5.18 NA ...
## $ Score_PSI_14 : num [1:4816] 1.69 1.87 1.76 NA NA 1.86 2.46 2.77 NA 1.83 ...
## $ Score_PSI_15 : num [1:4816] 0.93 0.91 1.34 1.08 NA 1.18 1.04 0.84 NA 0.88 ...
## $ Score_PSI_90 : num [1:4816] 1.21 0.97 1.17 0.95 NA 0.72 0.89 1.17 0.98 1.05 ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : chr [1:4816] "$22,212" "$18,030" "$21,898" NA ...Fixing the payment column
# Remove $ and , and convert to numeric
HipKneeClean <- HipKneeClean %>%
mutate_at(vars(starts_with("Payment_")),
~ as.numeric(gsub("[\\$,]", "", .)))
# Checking the structure
str(HipKneeClean)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 3 3 10 5 NA 6 10 9 NA 9 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : num [1:4816] 4 4 3 5 NA 3 4 4 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : num [1:4816] 3 2 2 4 NA 4 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : num [1:4816] 4 3 3 4 NA 3 3 2 NA 3 ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : num [1:4816] 3 2 1 2 NA 2 2 1 NA 4 ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : num [1:4816] 4 4 4 4 NA 4 4 3 NA 5 ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: num [1:4816] 4 3 2 4 NA 3 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : num [1:4816] 4 3 2 4 NA 4 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : num [1:4816] 89 90 88 91 NA 90 91 89 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : num [1:4816] 91 92 89 95 NA 90 91 91 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : num [1:4816] 81 75 75 88 NA 85 80 78 NA 85 ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : num [1:4816] 77 76 71 77 NA 76 76 72 NA 78 ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : num [1:4816] 87 86 83 87 NA 86 86 81 NA 86 ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : num [1:4816] 82 79 77 82 NA 81 79 80 NA 83 ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : num [1:4816] 84 80 74 80 NA 81 83 78 NA 88 ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : num [1:4816] 86 85 85 87 NA 84 84 82 NA 89 ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : num [1:4816] 89 85 82 89 NA 88 83 85 NA 90 ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : num [1:4816] 90 83 79 88 NA 87 80 84 NA 91 ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : num [1:4816] NA 148 NA NA NA NA NA NA NA NA ...
## $ HCP_COVID_19 : num [1:4816] 80.7 79.8 79 57.9 81.2 88 69.8 87.3 95.9 85.3 ...
## $ IMM_3 : num [1:4816] 95 80 67 53 45 81 65 93 98 81 ...
## $ OP_18b : num [1:4816] 215 147 177 130 118 206 160 185 102 145 ...
## $ OP_18c : num [1:4816] 317 266 NA 216 98 124 220 220 NA 324 ...
## $ OP_22 : num [1:4816] 5 3 1 4 0 5 4 3 0 2 ...
## $ OP_23 : num [1:4816] NA NA 69 NA NA 47 NA 73 NA 35 ...
## $ OP_29 : num [1:4816] 47 96 85 23 67 100 100 NA NA 82 ...
## $ SAFE_USE_OF_OPIOIDS : num [1:4816] 14 19 17 NA NA 20 14 23 NA 17 ...
## $ SEP_1 : num [1:4816] 66 74 56 86 NA 51 92 77 NA 87 ...
## $ SEP_SH_3HR : num [1:4816] 70 88 77 NA NA 78 94 83 NA 90 ...
## $ SEP_SH_6HR : num [1:4816] 100 91 81 NA NA 81 83 100 NA 94 ...
## $ SEV_SEP_3HR : num [1:4816] 79 88 78 89 NA 69 95 85 NA 94 ...
## $ SEV_SEP_6HR : num [1:4816] 95 96 86 97 NA 91 99 97 NA 99 ...
## $ STK_02 : num [1:4816] 98 100 96 NA NA 93 NA 99 NA NA ...
## $ STK_05 : num [1:4816] NA 91 NA NA NA NA NA NA NA NA ...
## $ STK_06 : num [1:4816] NA NA 97 NA NA NA NA NA NA NA ...
## $ VTE_1 : num [1:4816] 98 NA NA NA NA 79 89 84 44 59 ...
## $ VTE_2 : num [1:4816] 99 NA 97 NA NA 88 93 94 NA NA ...
## $ Score_COMP_HIP_KNEE : num [1:4816] 2.7 2.3 4.6 NA NA 3.5 3.8 3.5 NA 4.3 ...
## $ Score_MORT_30_AMI : num [1:4816] 12 13.6 16.5 NA NA 13.2 13.8 13.1 NA NA ...
## $ Score_MORT_30_COPD : num [1:4816] 8.8 9.9 9.9 13.7 NA 10.3 NA 9.2 NA 7.8 ...
## $ Score_MORT_30_HF : num [1:4816] 8.9 14.9 12.5 12.5 NA 13.5 13.6 9.9 NA 16.9 ...
## $ Score_MORT_30_PN : num [1:4816] 18 23.3 19.5 28.5 NA 20.9 22 17.2 NA 26.1 ...
## $ Score_MORT_30_STK : num [1:4816] 14.8 15.3 17.2 NA NA 12.3 NA 13.2 NA 17.3 ...
## $ Score_PSI_03 : num [1:4816] 0.39 0.94 1.39 0.42 0.54 0.13 0.41 0.63 0.57 0.47 ...
## $ Score_PSI_04 : num [1:4816] 185 183 174 NA NA ...
## $ Score_PSI_06 : num [1:4816] 0.23 0.22 0.36 0.24 0.25 0.24 0.24 0.21 0.25 0.22 ...
## $ Score_PSI_08 : num [1:4816] 0.1 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 ...
## $ Score_PSI_09 : num [1:4816] 2.39 2.69 5.43 2.49 NA 1.88 2.44 3.29 2.44 2.58 ...
## $ Score_PSI_10 : num [1:4816] 1.14 1.37 1.26 1.57 NA 1.72 1.51 1.2 1.57 NA ...
## $ Score_PSI_11 : num [1:4816] 13.83 7.19 7.37 8.45 NA ...
## $ Score_PSI_12 : num [1:4816] 4.49 3.01 3.36 3.89 NA 3.04 3.32 3.67 3.56 5.63 ...
## $ Score_PSI_13 : num [1:4816] 8.05 4.46 4.37 5.19 NA 5.55 4.88 6.08 5.18 NA ...
## $ Score_PSI_14 : num [1:4816] 1.69 1.87 1.76 NA NA 1.86 2.46 2.77 NA 1.83 ...
## $ Score_PSI_15 : num [1:4816] 0.93 0.91 1.34 1.08 NA 1.18 1.04 0.84 NA 0.88 ...
## $ Score_PSI_90 : num [1:4816] 1.21 0.97 1.17 0.95 NA 0.72 0.89 1.17 0.98 1.05 ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : num [1:4816] 22212 18030 21898 NA NA ...Saving the data to use without having to clean it
save(HipKneeClean, file = "HipKneeClean.RData")Exploring the cleaned data
Generating visualizations (SE)
Creating a summary table for numeric variables
# Select numeric columns
numeric_columns <- select_if(HipKneeClean, is.numeric)
# Calculate descriptive statistics
descr_stats <- psych::describe(numeric_columns)
# Convert to a data frame
descr_stats_df <- as.data.frame(descr_stats)
# Display the table
kable(descr_stats_df, format = "html", caption = "Table 6. Descriptive Statistics for Numeric Variables in Cleaned Dataset") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ExcessReadmissionRatio_HIP-KNEE | 1 | 1838 | 1.004161e+00 | 0.1263979 | 0.9921 | 1.000079e+00 | 0.1119363 | 0.6159 | 1.5162 | 0.9003 | 0.3862731 | 0.7447069 | 0.0029483 |
| PredictedReadmissionRate_HIP-KNEE | 2 | 1838 | 4.546552e+00 | 0.9092848 | 4.4768 | 4.511130e+00 | 0.8590184 | 1.9279 | 8.5690 | 6.6411 | 0.4370579 | 0.4866461 | 0.0212093 |
| ExpectedReadmissionRate_HIP-KNEE | 3 | 1838 | 4.519903e+00 | 0.6637697 | 4.4544 | 4.484779e+00 | 0.6165392 | 2.6749 | 7.6240 | 4.9491 | 0.6361300 | 1.0010780 | 0.0154826 |
| NumberOfReadmissions_HIP-KNEE | 4 | 1838 | 8.098477e+00 | 7.8400996 | 7.0000 | 6.813859e+00 | 4.4478000 | 1.0000 | 125.0000 | 124.0000 | 4.5178495 | 40.4373466 | 0.1828727 |
| PatientSurveyStarRating_H_COMP_1_STAR_RATING | 5 | 3255 | 3.260215e+00 | 1.0059133 | 3.0000 | 3.241843e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | 0.0239346 | -0.4825494 | 0.0176313 |
| PatientSurveyStarRating_H_COMP_2_STAR_RATING | 6 | 3255 | 3.428264e+00 | 0.9474515 | 3.0000 | 3.450672e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.3484208 | -0.0771131 | 0.0166066 |
| PatientSurveyStarRating_H_COMP_3_STAR_RATING | 7 | 3255 | 3.372350e+00 | 1.0909348 | 4.0000 | 3.388100e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.2839572 | -0.8381418 | 0.0191216 |
| PatientSurveyStarRating_H_COMP_5_STAR_RATING | 8 | 3255 | 3.064516e+00 | 0.9126664 | 3.0000 | 3.062572e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.0135291 | -0.3800413 | 0.0159969 |
| PatientSurveyStarRating_H_COMP_6_STAR_RATING | 9 | 3255 | 3.388940e+00 | 0.9148777 | 3.0000 | 3.401919e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.3167335 | 0.0370958 | 0.0160357 |
| PatientSurveyStarRating_H_COMP_7_STAR_RATING | 10 | 3255 | 3.167742e+00 | 0.9963691 | 3.0000 | 3.144338e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.0232912 | -0.4736871 | 0.0174640 |
| PatientSurveyStarRating_H_CLEAN_STAR_RATING | 11 | 3255 | 3.049770e+00 | 1.1197420 | 3.0000 | 3.063724e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.1031912 | -0.6984520 | 0.0196265 |
| PatientSurveyStarRating_H_QUIET_STAR_RATING | 12 | 3255 | 3.214132e+00 | 1.1166932 | 3.0000 | 3.228791e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.1265578 | -0.6910097 | 0.0195730 |
| PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | 13 | 3255 | 3.243318e+00 | 0.9195166 | 3.0000 | 3.268330e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.2786674 | 0.0381366 | 0.0161170 |
| PatientSurveyStarRating_H_RECMND_STAR_RATING | 14 | 3255 | 3.497696e+00 | 1.0287408 | 4.0000 | 3.554702e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.6160258 | -0.1587475 | 0.0180314 |
| PatientSurveyStarRating_H_STAR_RATING | 15 | 3255 | 3.295545e+00 | 0.9142197 | 3.0000 | 3.294818e+00 | 1.4826000 | 1.0000 | 5.0000 | 4.0000 | -0.1485226 | -0.2457959 | 0.0160242 |
| HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | 16 | 3255 | 9.049002e+01 | 2.9117012 | 91.0000 | 9.063608e+01 | 2.9652000 | 77.0000 | 100.0000 | 23.0000 | -0.6770638 | 1.4141442 | 0.0510354 |
| HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | 17 | 3255 | 9.028879e+01 | 2.8233453 | 90.0000 | 9.039079e+01 | 2.9652000 | 76.0000 | 100.0000 | 24.0000 | -0.4886223 | 1.1133723 | 0.0494867 |
| HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | 18 | 3255 | 8.276897e+01 | 5.3062993 | 83.0000 | 8.280499e+01 | 4.4478000 | 61.0000 | 100.0000 | 39.0000 | -0.1793486 | 0.4429645 | 0.0930071 |
| HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | 19 | 3255 | 7.554593e+01 | 5.1600947 | 75.0000 | 7.547178e+01 | 4.4478000 | 51.0000 | 99.0000 | 48.0000 | 0.0989232 | 0.6200355 | 0.0904445 |
| HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | 20 | 3255 | 8.568786e+01 | 4.1729980 | 86.0000 | 8.596238e+01 | 2.9652000 | 59.0000 | 100.0000 | 41.0000 | -0.9067227 | 2.2178795 | 0.0731430 |
| HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | 21 | 3255 | 8.040277e+01 | 3.2911839 | 81.0000 | 8.046795e+01 | 2.9652000 | 64.0000 | 97.0000 | 33.0000 | -0.2792695 | 1.0404404 | 0.0576868 |
| HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | 22 | 3255 | 8.560799e+01 | 4.7038213 | 86.0000 | 8.575931e+01 | 4.4478000 | 68.0000 | 99.0000 | 31.0000 | -0.3531253 | 0.1936870 | 0.0824471 |
| HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | 23 | 3255 | 8.172657e+01 | 5.6927220 | 82.0000 | 8.193282e+01 | 5.9304000 | 56.0000 | 99.0000 | 43.0000 | -0.3843168 | 0.2536999 | 0.0997802 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 24 | 3255 | 8.709708e+01 | 4.0649053 | 88.0000 | 8.730940e+01 | 2.9652000 | 65.0000 | 98.0000 | 33.0000 | -0.6979236 | 1.3325347 | 0.0712484 |
| HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | 25 | 3255 | 8.607343e+01 | 5.2109599 | 87.0000 | 8.638349e+01 | 4.4478000 | 57.0000 | 99.0000 | 42.0000 | -0.7407627 | 1.2385658 | 0.0913361 |
| ED_2_Strata_1 | 26 | 1107 | 1.064002e+02 | 114.0608548 | 74.0000 | 8.602368e+01 | 50.4084000 | 0.0000 | 1078.0000 | 1078.0000 | 3.9796326 | 22.8953094 | 3.4281736 |
| HCP_COVID_19 | 27 | 3633 | 8.767556e+01 | 10.6218376 | 90.1000 | 8.903068e+01 | 9.4886400 | 0.5000 | 100.0000 | 99.5000 | -1.4380632 | 3.5511860 | 0.1762248 |
| IMM_3 | 28 | 4140 | 7.782681e+01 | 18.5753061 | 83.0000 | 8.024245e+01 | 17.7912000 | 0.0000 | 100.0000 | 100.0000 | -1.0622115 | 0.7180171 | 0.2886927 |
| OP_18b | 29 | 4067 | 1.617780e+02 | 54.6367977 | 153.0000 | 1.572833e+02 | 53.3736000 | 38.0000 | 587.0000 | 549.0000 | 0.9959452 | 2.0284358 | 0.8567382 |
| OP_18c | 30 | 3098 | 2.967434e+02 | 177.2966416 | 255.0000 | 2.700827e+02 | 117.1254000 | 40.0000 | 2954.0000 | 2914.0000 | 3.5082626 | 28.6044027 | 3.1853694 |
| OP_22 | 31 | 3841 | 2.385056e+00 | 2.3270098 | 2.0000 | 2.028637e+00 | 1.4826000 | 0.0000 | 19.0000 | 19.0000 | 1.7797727 | 4.6722341 | 0.0375471 |
| OP_23 | 32 | 1535 | 7.062801e+01 | 19.2269197 | 74.0000 | 7.247193e+01 | 17.7912000 | 0.0000 | 100.0000 | 100.0000 | -0.9377173 | 0.8863709 | 0.4907446 |
| OP_29 | 33 | 2830 | 9.125230e+01 | 14.2952983 | 96.0000 | 9.458083e+01 | 5.9304000 | 0.0000 | 100.0000 | 100.0000 | -3.1390450 | 11.9252917 | 0.2687200 |
| SAFE_USE_OF_OPIOIDS | 34 | 3670 | 1.561226e+01 | 5.6808277 | 15.0000 | 1.537568e+01 | 4.4478000 | 0.0000 | 45.0000 | 45.0000 | 0.6530895 | 2.0207900 | 0.0937732 |
| SEP_1 | 35 | 3097 | 5.982661e+01 | 16.7144073 | 61.0000 | 6.045180e+01 | 16.3086000 | 0.0000 | 100.0000 | 100.0000 | -0.4029034 | 0.1050294 | 0.3003450 |
| SEP_SH_3HR | 36 | 2620 | 6.724809e+01 | 17.8935243 | 68.0000 | 6.776813e+01 | 19.2738000 | 0.0000 | 100.0000 | 100.0000 | -0.2914787 | -0.2730680 | 0.3495789 |
| SEP_SH_6HR | 37 | 2039 | 8.305983e+01 | 15.4244924 | 87.0000 | 8.529455e+01 | 11.8608000 | 7.0000 | 100.0000 | 93.0000 | -1.5023285 | 2.7030480 | 0.3415877 |
| SEV_SEP_3HR | 38 | 3086 | 7.904342e+01 | 11.1773414 | 81.0000 | 7.998907e+01 | 10.3782000 | 0.0000 | 100.0000 | 100.0000 | -1.4060736 | 4.9567276 | 0.2012058 |
| SEV_SEP_6HR | 39 | 2937 | 8.871263e+01 | 11.3390205 | 92.0000 | 9.061974e+01 | 7.4130000 | 0.0000 | 100.0000 | 100.0000 | -2.4051707 | 8.9993568 | 0.2092298 |
| STK_02 | 40 | 1537 | 9.529733e+01 | 6.1635225 | 97.0000 | 9.638262e+01 | 2.9652000 | 23.0000 | 100.0000 | 77.0000 | -4.7304997 | 35.8413538 | 0.1572143 |
| STK_05 | 41 | 1094 | 9.278702e+01 | 7.1402994 | 94.0000 | 9.367352e+01 | 4.4478000 | 2.0000 | 100.0000 | 98.0000 | -5.4512524 | 54.5350543 | 0.2158777 |
| STK_06 | 42 | 1298 | 9.464946e+01 | 7.5064354 | 96.0000 | 9.581154e+01 | 2.9652000 | 0.0000 | 100.0000 | 100.0000 | -7.3728956 | 77.6048723 | 0.2083514 |
| VTE_1 | 43 | 2216 | 8.246435e+01 | 19.1503872 | 89.0000 | 8.603777e+01 | 11.8608000 | 0.0000 | 100.0000 | 100.0000 | -1.7514646 | 3.0387995 | 0.4068110 |
| VTE_2 | 44 | 1413 | 9.383015e+01 | 9.7362977 | 97.0000 | 9.588241e+01 | 2.9652000 | 3.0000 | 100.0000 | 97.0000 | -4.1429487 | 23.6985405 | 0.2590137 |
| Score_COMP_HIP_KNEE | 45 | 2090 | 3.182392e+00 | 0.5482694 | 3.1000 | 3.150419e+00 | 0.4447800 | 1.6000 | 6.2000 | 4.6000 | 0.7716603 | 1.9037431 | 0.0119928 |
| Score_MORT_30_AMI | 46 | 1943 | 1.254359e+01 | 1.1553168 | 12.5000 | 1.251608e+01 | 1.0378200 | 8.9000 | 17.1000 | 8.2000 | 0.2785565 | 0.5897728 | 0.0262099 |
| Score_MORT_30_COPD | 47 | 2569 | 9.185286e+00 | 1.3614554 | 9.1000 | 9.121196e+00 | 1.3343400 | 5.2000 | 14.9000 | 9.7000 | 0.5044944 | 0.5326934 | 0.0268609 |
| Score_MORT_30_HF | 48 | 3056 | 1.182863e+01 | 1.9384358 | 11.8000 | 1.180581e+01 | 1.7791200 | 5.5000 | 20.4000 | 14.9000 | 0.1359787 | 0.4028740 | 0.0350651 |
| Score_MORT_30_PN | 49 | 3514 | 1.833056e+01 | 2.5441335 | 18.2000 | 1.826543e+01 | 2.3721600 | 8.6000 | 29.5000 | 20.9000 | 0.3130182 | 0.5748975 | 0.0429180 |
| Score_MORT_30_STK | 50 | 2123 | 1.379157e+01 | 1.8194129 | 13.7000 | 1.371648e+01 | 1.7791200 | 8.0000 | 21.9000 | 13.9000 | 0.4400162 | 0.5676934 | 0.0394872 |
| Score_PSI_03 | 51 | 3169 | 5.805491e-01 | 0.4702323 | 0.4800 | 5.037288e-01 | 0.2372160 | 0.0500 | 6.3100 | 6.2600 | 4.0520349 | 30.4735061 | 0.0083532 |
| Score_PSI_04 | 52 | 1609 | 1.687290e+02 | 21.3153769 | 167.7400 | 1.687267e+02 | 20.2523160 | 86.6800 | 241.8100 | 155.1300 | -0.0315953 | 0.4882789 | 0.5313920 |
| Score_PSI_06 | 53 | 3188 | 2.476851e-01 | 0.0402023 | 0.2400 | 2.442712e-01 | 0.0296520 | 0.1200 | 0.5100 | 0.3900 | 1.1937679 | 3.4906535 | 0.0007120 |
| Score_PSI_08 | 54 | 3189 | 9.043270e-02 | 0.0070889 | 0.0900 | 9.019980e-02 | 0.0000000 | 0.0600 | 0.1300 | 0.0700 | 0.5878153 | 2.8393103 | 0.0001255 |
| Score_PSI_09 | 55 | 2930 | 2.508707e+00 | 0.4395922 | 2.4600 | 2.478486e+00 | 0.2668680 | 1.1000 | 6.1000 | 5.0000 | 1.3622221 | 5.9305415 | 0.0081211 |
| Score_PSI_10 | 56 | 2593 | 1.569626e+00 | 0.3418816 | 1.5300 | 1.535055e+00 | 0.1186080 | 0.4700 | 4.5500 | 4.0800 | 1.9801686 | 8.7853292 | 0.0067139 |
| Score_PSI_11 | 57 | 2603 | 9.045517e+00 | 3.2148329 | 8.3900 | 8.740322e+00 | 2.1201180 | 2.7300 | 66.8500 | 64.1200 | 4.3362544 | 54.8289666 | 0.0630117 |
| Score_PSI_12 | 58 | 2935 | 3.597278e+00 | 0.7194093 | 3.5000 | 3.542005e+00 | 0.5633880 | 1.6100 | 7.5100 | 5.9000 | 1.0157663 | 2.2968831 | 0.0132792 |
| Score_PSI_13 | 59 | 2549 | 5.298133e+00 | 0.9887454 | 5.1300 | 5.224669e+00 | 0.7116480 | 2.1700 | 13.4900 | 11.3200 | 1.1662305 | 4.3263395 | 0.0195839 |
| Score_PSI_14 | 60 | 2592 | 2.010590e+00 | 0.3338405 | 1.9400 | 1.969769e+00 | 0.1482600 | 0.8900 | 4.4000 | 3.5100 | 1.9779060 | 7.1675818 | 0.0065572 |
| Score_PSI_15 | 61 | 2916 | 1.101708e+00 | 0.2939729 | 1.0500 | 1.067549e+00 | 0.1630860 | 0.3500 | 3.4300 | 3.0800 | 1.8219347 | 6.3874487 | 0.0054439 |
| Score_PSI_90 | 62 | 3011 | 1.001588e+00 | 0.1793301 | 0.9700 | 9.839477e-01 | 0.1186080 | 0.5500 | 2.7400 | 2.1900 | 2.0610890 | 10.6309961 | 0.0032681 |
| Payment_PAYM_90_HIP_KNEE | 63 | 2001 | 2.105813e+04 | 2079.2072318 | 20899.0000 | 2.093031e+04 | 1756.8810000 | 15936.0000 | 48153.0000 | 32217.0000 | 1.7757439 | 15.5385065 | 46.4808683 |
Exploring categorical variables
# Visualizing the distribution of EDV (Emergency Department Volume)
ggplot(HipKneeClean, aes(x = EDV)) +
geom_bar(fill = "skyblue", color = "black", alpha = 0.7) +
labs(title = "Figure 1. Distribution of Emergency Department Volume",
x = "EDV",
y = "Count") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
Visualizing the number of facilities per state
# Data preparation
facility_counts <- HipKneeClean %>%
group_by(State) %>%
summarise(Count = n(), .groups = 'drop')
# Check the first few rows
head(facility_counts)## # A tibble: 6 × 2
## State Count
## <chr> <int>
## 1 AK 21
## 2 AL 88
## 3 AR 79
## 4 AS 1
## 5 AZ 82
## 6 CA 327# Get state boundaries
states_map <- map_data("state")
# Create a mapping from state abbreviations to full state names
state_mapping <- data.frame(
State = state.abb,
full_state_name = tolower(state.name),
stringsAsFactors = FALSE
)
# Add full state names to facility_counts
facility_counts <- merge(facility_counts, state_mapping, by.x = "State", by.y = "State")
# Join facility counts with state map data
facility_map_data <- left_join(states_map, facility_counts, by = c("region" = "full_state_name"))
# Replace NA values with 0 in the Count column
facility_map_data$Count[is.na(facility_map_data$Count)] <- 0
# Plot the map with facility counts
ggplot(data = facility_map_data) +
geom_polygon(aes(x = long, y = lat, group = group, fill = Count), color = "white") +
scale_fill_gradient(low = "lightblue", high = "darkblue", na.value = "grey50", name = "Facility Count") +
theme_minimal() +
labs(title = "Figure 2. Number of Facilities per State") +
theme(axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.background = element_blank())
Visualizing the average PredictedReadmissionRate_HIP-KNEE per state
# Rename column
HipKneeClean <- HipKneeClean %>%
rename(PredictedReadmissionRate_HIP_KNEE = `PredictedReadmissionRate_HIP-KNEE`)
# Calculate the average PredictedReadmissionRate_HIP-KNEE per state
average_readmission_rate <- HipKneeClean %>%
group_by(State) %>%
summarize(Average_PredictedReadmissionRate_HIP_KNEE = mean(PredictedReadmissionRate_HIP_KNEE, na.rm = TRUE))
# Add full state names to the average readmission rate data
average_readmission_rate <- merge(average_readmission_rate, state_mapping, by.x = "State", by.y = "State")
# Join average readmission rate with state map data
readmission_map_data <- left_join(states_map, average_readmission_rate, by = c("region" = "full_state_name"))
# Plot the map with average readmission rates
ggplot(data = readmission_map_data) +
geom_polygon(aes(x = long, y = lat, group = group, fill = Average_PredictedReadmissionRate_HIP_KNEE), color = "white") +
scale_fill_gradient(low = "lightgreen", high = "darkgreen", name = "Average Predicted\nReadmission Rate") +
theme_minimal() +
labs(title = "Figure 3. Average Predicted Readmission Rate for Hip/Knee Replacement per State") +
theme(axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.background = element_blank())
Visualizing the spread of the target variable (PredictedReadmissionRate_HIP_KNEE)
# Create a histogram of PredictedReadmissionRate_HIP_KNEE
ggplot(HipKneeClean, aes(x = PredictedReadmissionRate_HIP_KNEE)) +
geom_histogram(binwidth = 0.25, fill = "skyblue", color = "black") +
labs(title = "Figure 4. Histogram of Predicted Readmission Rate for Hip/Knee Replacement",
x = "Predicted Readmission Rate",
y = "Frequency") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())## Warning: Removed 2978 rows containing non-finite values (`stat_bin()`).
Creating a table of missing values
# Calculate missing values
missing_values_summary <- HipKneeClean %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeClean)) * 100)
# Print the table using kable
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0.000000 |
| ExcessReadmissionRatio_HIP-KNEE | 2978 | 61.835548 |
| PredictedReadmissionRate_HIP_KNEE | 2978 | 61.835548 |
| ExpectedReadmissionRate_HIP-KNEE | 2978 | 61.835548 |
| NumberOfReadmissions_HIP-KNEE | 2978 | 61.835548 |
| PatientSurveyStarRating_H_COMP_1_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_2_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_3_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_5_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_6_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_COMP_7_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_CLEAN_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_QUIET_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_RECMND_STAR_RATING | 1561 | 32.412791 |
| PatientSurveyStarRating_H_STAR_RATING | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 1561 | 32.412791 |
| HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | 1561 | 32.412791 |
| EDV | 972 | 20.182724 |
| ED_2_Strata_1 | 3709 | 77.014120 |
| HCP_COVID_19 | 1183 | 24.563954 |
| IMM_3 | 676 | 14.036545 |
| OP_18b | 749 | 15.552326 |
| OP_18c | 1718 | 35.672758 |
| OP_22 | 975 | 20.245017 |
| OP_23 | 3281 | 68.127076 |
| OP_29 | 1986 | 41.237541 |
| SAFE_USE_OF_OPIOIDS | 1146 | 23.795681 |
| SEP_1 | 1719 | 35.693522 |
| SEP_SH_3HR | 2196 | 45.598007 |
| SEP_SH_6HR | 2777 | 57.661960 |
| SEV_SEP_3HR | 1730 | 35.921927 |
| SEV_SEP_6HR | 1879 | 39.015781 |
| STK_02 | 3279 | 68.085548 |
| STK_05 | 3722 | 77.284053 |
| STK_06 | 3518 | 73.048173 |
| VTE_1 | 2600 | 53.986711 |
| VTE_2 | 3403 | 70.660299 |
| Score_COMP_HIP_KNEE | 2726 | 56.602990 |
| Score_MORT_30_AMI | 2873 | 59.655316 |
| Score_MORT_30_COPD | 2247 | 46.656977 |
| Score_MORT_30_HF | 1760 | 36.544851 |
| Score_MORT_30_PN | 1302 | 27.034884 |
| Score_MORT_30_STK | 2693 | 55.917774 |
| Score_PSI_03 | 1647 | 34.198505 |
| Score_PSI_04 | 3207 | 66.590532 |
| Score_PSI_06 | 1628 | 33.803987 |
| Score_PSI_08 | 1627 | 33.783223 |
| Score_PSI_09 | 1886 | 39.161130 |
| Score_PSI_10 | 2223 | 46.158638 |
| Score_PSI_11 | 2213 | 45.950997 |
| Score_PSI_12 | 1881 | 39.057309 |
| Score_PSI_13 | 2267 | 47.072259 |
| Score_PSI_14 | 2224 | 46.179402 |
| Score_PSI_15 | 1900 | 39.451827 |
| Score_PSI_90 | 1805 | 37.479236 |
| FacilityName | 171 | 3.550664 |
| State | 171 | 3.550664 |
| Payment_PAYM_90_HIP_KNEE | 2815 | 58.450997 |
Assessing collinearity
# Compute correlation matrix
cor_matrix <- cor(HipKneeClean %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot the heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
# Convert the correlation matrix to a data frame
cor_table <- as.data.frame(cor_matrix)
# Add variable names as a column for better readability
cor_table$Variable <- rownames(cor_table)
# Reorder columns for better readability
cor_table <- cor_table %>%
select(Variable, everything())
# Print the table using kable
cor_table %>%
kable(caption = "Table 8. Correlation Coefficients Table") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | ExcessReadmissionRatio_HIP-KNEE | PredictedReadmissionRate_HIP_KNEE | ExpectedReadmissionRate_HIP-KNEE | NumberOfReadmissions_HIP-KNEE | PatientSurveyStarRating_H_COMP_1_STAR_RATING | PatientSurveyStarRating_H_COMP_2_STAR_RATING | PatientSurveyStarRating_H_COMP_3_STAR_RATING | PatientSurveyStarRating_H_COMP_5_STAR_RATING | PatientSurveyStarRating_H_COMP_6_STAR_RATING | PatientSurveyStarRating_H_COMP_7_STAR_RATING | PatientSurveyStarRating_H_CLEAN_STAR_RATING | PatientSurveyStarRating_H_QUIET_STAR_RATING | PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | PatientSurveyStarRating_H_RECMND_STAR_RATING | PatientSurveyStarRating_H_STAR_RATING | HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | ED_2_Strata_1 | HCP_COVID_19 | IMM_3 | OP_18b | OP_18c | OP_22 | OP_23 | OP_29 | SAFE_USE_OF_OPIOIDS | SEP_1 | SEP_SH_3HR | SEP_SH_6HR | SEV_SEP_3HR | SEV_SEP_6HR | STK_02 | STK_05 | STK_06 | VTE_1 | VTE_2 | Score_COMP_HIP_KNEE | Score_MORT_30_AMI | Score_MORT_30_COPD | Score_MORT_30_HF | Score_MORT_30_PN | Score_MORT_30_STK | Score_PSI_03 | Score_PSI_04 | Score_PSI_06 | Score_PSI_08 | Score_PSI_09 | Score_PSI_10 | Score_PSI_11 | Score_PSI_12 | Score_PSI_13 | Score_PSI_14 | Score_PSI_15 | Score_PSI_90 | Payment_PAYM_90_HIP_KNEE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ExcessReadmissionRatio_HIP-KNEE | ExcessReadmissionRatio_HIP-KNEE | 1.0000000 | 0.6851738 | 0.0934639 | 0.0280292 | -0.1590972 | -0.1749847 | -0.1597408 | -0.1551935 | -0.1771868 | -0.1941803 | -0.1160735 | -0.1047116 | -0.1709175 | -0.1659007 | -0.1783759 | -0.1666746 | -0.1802262 | -0.1831908 | -0.1775098 | -0.1844376 | -0.1877798 | -0.1223047 | -0.1123794 | -0.1779322 | -0.1852873 | 0.0762327 | -0.0508152 | -0.0583326 | 0.0505288 | 0.0838395 | 0.0252830 | 0.0485500 | -0.0296026 | 0.0627005 | -0.0309171 | -0.0529663 | 0.0313734 | -0.0322822 | -0.0278551 | -0.0434539 | -0.0205857 | -0.0425563 | 0.0039309 | 0.0707936 | 0.4350513 | 0.0392653 | -0.0452304 | -0.0577013 | -0.0046351 | -0.0025521 | -0.0058883 | 0.0129772 | 0.0156305 | 0.0386634 | -0.0459321 | 0.0099239 | 0.1103031 | 0.0939499 | 0.1262199 | -0.0138581 | -0.0019014 | 0.0882354 | 0.2740999 |
| PredictedReadmissionRate_HIP_KNEE | PredictedReadmissionRate_HIP_KNEE | 0.6851738 | 1.0000000 | 0.7840403 | -0.0298799 | -0.2144148 | -0.2287902 | -0.2138329 | -0.2191170 | -0.2029456 | -0.2247172 | -0.1981594 | -0.1748506 | -0.2016141 | -0.1642097 | -0.2308937 | -0.2067264 | -0.2352248 | -0.2449572 | -0.2546502 | -0.2079982 | -0.2250397 | -0.2045779 | -0.1801665 | -0.2060912 | -0.1911254 | 0.1082272 | -0.0563082 | -0.0028840 | 0.1295727 | 0.0877808 | 0.0501727 | 0.0614226 | -0.0106510 | 0.1063002 | -0.0326387 | -0.0689850 | 0.0231777 | -0.0375511 | -0.0014846 | 0.0066866 | -0.0801055 | -0.0101158 | 0.0654668 | 0.1064994 | 0.3208550 | 0.0074065 | -0.0794948 | -0.1067828 | -0.0985660 | -0.0376746 | -0.0037334 | -0.0449077 | 0.0154891 | -0.0214412 | -0.0182303 | 0.0710046 | 0.1130121 | 0.1047402 | 0.1193336 | 0.0140012 | -0.0158282 | 0.0973882 | 0.2975679 |
| ExpectedReadmissionRate_HIP-KNEE | ExpectedReadmissionRate_HIP-KNEE | 0.0934639 | 0.7840403 | 1.0000000 | -0.0742574 | -0.1696704 | -0.1755397 | -0.1644998 | -0.1788949 | -0.1353119 | -0.1533717 | -0.1829249 | -0.1621036 | -0.1408372 | -0.0923845 | -0.1744075 | -0.1524571 | -0.1798454 | -0.1926175 | -0.2093859 | -0.1348924 | -0.1608376 | -0.1848471 | -0.1649195 | -0.1425272 | -0.1150026 | 0.0843876 | -0.0316503 | 0.0435410 | 0.1403298 | 0.0491515 | 0.0510729 | 0.0366382 | 0.0098417 | 0.0946020 | -0.0189720 | -0.0503969 | 0.0048063 | -0.0227213 | 0.0212143 | 0.0415201 | -0.0867129 | 0.0212012 | 0.0866767 | 0.0781368 | 0.0742579 | -0.0287661 | -0.0683850 | -0.0967183 | -0.1319780 | -0.0478311 | 0.0027232 | -0.0749480 | 0.0017920 | -0.0595834 | 0.0136214 | 0.0894074 | 0.0639191 | 0.0654692 | 0.0603447 | 0.0328153 | -0.0242059 | 0.0626347 | 0.1808580 |
| NumberOfReadmissions_HIP-KNEE | NumberOfReadmissions_HIP-KNEE | 0.0280292 | -0.0298799 | -0.0742574 | 1.0000000 | 0.0798078 | 0.0740972 | 0.0227895 | 0.0398242 | 0.0599664 | 0.1063912 | 0.0109790 | 0.0177193 | 0.1181224 | 0.1391159 | 0.0779986 | 0.0692166 | 0.0716989 | 0.0290342 | 0.0495607 | 0.0542992 | 0.1106351 | 0.0187289 | 0.0248142 | 0.1116353 | 0.1516789 | 0.0425263 | 0.0450149 | 0.0255192 | 0.0895220 | 0.0668517 | 0.0478896 | -0.0553207 | -0.0126112 | 0.0749623 | 0.0004693 | -0.0353431 | 0.0408255 | 0.0074947 | 0.0166986 | 0.0343487 | -0.0087498 | 0.0347316 | 0.0586463 | 0.0734882 | -0.1517362 | -0.1650703 | -0.0843669 | -0.1375901 | -0.1231721 | -0.1345921 | -0.0339593 | -0.0798121 | -0.0499866 | -0.1384081 | -0.0209899 | -0.0728092 | -0.0689026 | -0.0448292 | -0.0832889 | -0.0662610 | -0.0665941 | -0.0942117 | -0.1234255 |
| PatientSurveyStarRating_H_COMP_1_STAR_RATING | PatientSurveyStarRating_H_COMP_1_STAR_RATING | -0.1590972 | -0.2144148 | -0.1696704 | 0.0798078 | 1.0000000 | 0.7652622 | 0.8092146 | 0.7817407 | 0.6947694 | 0.8094552 | 0.5857397 | 0.6176477 | 0.8113195 | 0.7410800 | 0.8821074 | 0.9413741 | 0.7947199 | 0.8439662 | 0.8192660 | 0.7212981 | 0.8279021 | 0.6062342 | 0.6396807 | 0.8361084 | 0.7761632 | -0.3811431 | -0.0217341 | 0.1991033 | -0.3200784 | -0.1816118 | -0.2152685 | 0.0440556 | 0.0700383 | 0.1401327 | 0.1288469 | 0.0438649 | -0.0683424 | 0.1642083 | 0.1503950 | 0.2176041 | 0.1753654 | 0.1870897 | -0.0859359 | -0.0029321 | -0.0761301 | -0.0479940 | -0.0124762 | 0.1078488 | 0.0183801 | -0.0132887 | -0.0219430 | -0.0320167 | -0.0184090 | 0.0046992 | 0.0835767 | -0.0402520 | -0.1459187 | -0.0669148 | -0.1424419 | 0.0017770 | 0.0329127 | -0.1122411 | -0.2121841 |
| PatientSurveyStarRating_H_COMP_2_STAR_RATING | PatientSurveyStarRating_H_COMP_2_STAR_RATING | -0.1749847 | -0.2287902 | -0.1755397 | 0.0740972 | 0.7652622 | 1.0000000 | 0.6820394 | 0.7310740 | 0.6334942 | 0.7745158 | 0.4946512 | 0.5958859 | 0.7576397 | 0.7012869 | 0.8135436 | 0.7932584 | 0.9499147 | 0.7139678 | 0.7675074 | 0.6648163 | 0.8027744 | 0.5120334 | 0.6187305 | 0.7826816 | 0.7365285 | -0.2976284 | 0.0206259 | 0.2149395 | -0.2473453 | -0.1452767 | -0.1462753 | 0.0003840 | 0.0721589 | 0.0759631 | 0.0920878 | 0.0418658 | -0.0697479 | 0.1295914 | 0.0899079 | 0.1940882 | 0.1763581 | 0.1579604 | -0.0887946 | -0.0231504 | -0.0615070 | -0.0650654 | -0.0163367 | 0.0722015 | -0.0021513 | -0.0165143 | -0.0016914 | -0.0041080 | 0.0301520 | -0.0171646 | 0.0956403 | -0.0267891 | -0.1367919 | -0.0390690 | -0.1430591 | -0.0079719 | 0.0359419 | -0.0871039 | -0.1893123 |
| PatientSurveyStarRating_H_COMP_3_STAR_RATING | PatientSurveyStarRating_H_COMP_3_STAR_RATING | -0.1597408 | -0.2138329 | -0.1644998 | 0.0227895 | 0.8092146 | 0.6820394 | 1.0000000 | 0.7583026 | 0.6569534 | 0.7355634 | 0.5956464 | 0.6138696 | 0.7478412 | 0.6650712 | 0.8260329 | 0.8314522 | 0.7052054 | 0.9423558 | 0.7878299 | 0.6812130 | 0.7546488 | 0.6155860 | 0.6319284 | 0.7774308 | 0.6961148 | -0.3896187 | -0.0747053 | 0.1498815 | -0.3981251 | -0.2241446 | -0.2360570 | 0.0486530 | 0.0507759 | 0.0900327 | 0.1321630 | 0.0521192 | -0.0725481 | 0.1605381 | 0.1368127 | 0.1201354 | 0.1413712 | 0.0905514 | -0.0996751 | -0.0477865 | -0.0405313 | -0.0177776 | 0.0302290 | 0.1568119 | 0.0384967 | 0.0435642 | -0.0192053 | -0.0016542 | 0.0274588 | 0.0159553 | 0.0859168 | -0.0262995 | -0.1292050 | -0.0865925 | -0.1330731 | 0.0092188 | 0.0480143 | -0.1032756 | -0.1564699 |
| PatientSurveyStarRating_H_COMP_5_STAR_RATING | PatientSurveyStarRating_H_COMP_5_STAR_RATING | -0.1551935 | -0.2191170 | -0.1788949 | 0.0398242 | 0.7817407 | 0.7310740 | 0.7583026 | 1.0000000 | 0.6659220 | 0.7694318 | 0.5769327 | 0.5922228 | 0.7502954 | 0.6685334 | 0.8320006 | 0.8038535 | 0.7587947 | 0.7931167 | 0.9410632 | 0.6945471 | 0.7903879 | 0.5987567 | 0.6129310 | 0.7793734 | 0.7066986 | -0.3575914 | -0.0062821 | 0.1693415 | -0.3234673 | -0.1918556 | -0.1939137 | 0.0438526 | 0.0664748 | 0.0784938 | 0.1324347 | 0.0587805 | -0.0562912 | 0.1606626 | 0.1423359 | 0.1358181 | 0.1964279 | 0.1045092 | -0.0999738 | -0.0151359 | -0.0450744 | -0.0510480 | -0.0130589 | 0.0644025 | -0.0046993 | 0.0257903 | -0.0221085 | -0.0185639 | 0.0273930 | -0.0011887 | 0.0897240 | -0.0322016 | -0.1375039 | -0.0537461 | -0.1257130 | -0.0064032 | 0.0443475 | -0.1023631 | -0.1576660 |
| PatientSurveyStarRating_H_COMP_6_STAR_RATING | PatientSurveyStarRating_H_COMP_6_STAR_RATING | -0.1771868 | -0.2029456 | -0.1353119 | 0.0599664 | 0.6947694 | 0.6334942 | 0.6569534 | 0.6659220 | 1.0000000 | 0.7186423 | 0.4784757 | 0.4292218 | 0.6769019 | 0.6409371 | 0.7586740 | 0.7386381 | 0.6584544 | 0.6847212 | 0.7015421 | 0.9400388 | 0.7549683 | 0.4999730 | 0.4567781 | 0.7098506 | 0.6770439 | -0.3008610 | 0.0354071 | 0.2404893 | -0.2071783 | -0.1351116 | -0.1225568 | 0.0625152 | 0.1045421 | 0.1184473 | 0.1443955 | 0.0557260 | -0.0637431 | 0.1782560 | 0.1758781 | 0.2091327 | 0.2034132 | 0.1930126 | 0.0235381 | 0.0288319 | -0.0990420 | -0.0682603 | 0.0034724 | 0.1194712 | -0.0343594 | 0.0159051 | 0.0012755 | 0.0047573 | 0.0312923 | -0.0120380 | 0.0883277 | -0.0190535 | -0.1484276 | -0.0612852 | -0.1424055 | -0.0023314 | 0.0662158 | -0.0911960 | -0.2089917 |
| PatientSurveyStarRating_H_COMP_7_STAR_RATING | PatientSurveyStarRating_H_COMP_7_STAR_RATING | -0.1941803 | -0.2247172 | -0.1533717 | 0.1063912 | 0.8094552 | 0.7745158 | 0.7355634 | 0.7694318 | 0.7186423 | 1.0000000 | 0.5720636 | 0.6101555 | 0.8272215 | 0.7939928 | 0.8743494 | 0.8277780 | 0.7995932 | 0.7605870 | 0.8011319 | 0.7433629 | 0.9482189 | 0.5929605 | 0.6371231 | 0.8571093 | 0.8310741 | -0.3553994 | 0.0397427 | 0.2374707 | -0.2572208 | -0.1626711 | -0.2149719 | 0.0359131 | 0.0858822 | 0.1201701 | 0.1393844 | 0.0474753 | -0.0250525 | 0.1732976 | 0.1422983 | 0.2157600 | 0.1380616 | 0.1802407 | -0.0364693 | 0.0393084 | -0.1067242 | -0.1098730 | -0.0673445 | 0.0151872 | -0.0880905 | -0.0653713 | -0.0300329 | -0.0817158 | -0.0009316 | -0.0348072 | 0.0827573 | -0.0399586 | -0.1668264 | -0.0670446 | -0.1593474 | -0.0164980 | 0.0357574 | -0.1311288 | -0.1977109 |
| PatientSurveyStarRating_H_CLEAN_STAR_RATING | PatientSurveyStarRating_H_CLEAN_STAR_RATING | -0.1160735 | -0.1981594 | -0.1829249 | 0.0109790 | 0.5857397 | 0.4946512 | 0.5956464 | 0.5769327 | 0.4784757 | 0.5720636 | 1.0000000 | 0.4987457 | 0.5965227 | 0.5237951 | 0.6391671 | 0.5928460 | 0.5111389 | 0.6220272 | 0.5982789 | 0.4927072 | 0.5781200 | 0.9570846 | 0.5105668 | 0.6248221 | 0.5505967 | -0.3195008 | -0.0225176 | 0.0814169 | -0.3267508 | -0.1804160 | -0.2479612 | 0.0190913 | 0.0137026 | 0.0598688 | 0.1690161 | 0.0858056 | -0.0192334 | 0.1869822 | 0.1376097 | 0.0367533 | 0.0909053 | 0.0153703 | -0.0848392 | -0.0027352 | -0.0571772 | -0.0630079 | -0.0352455 | 0.0665845 | 0.0009844 | -0.0651509 | -0.0629368 | -0.1272277 | -0.0390292 | 0.0016051 | -0.0069280 | -0.0851994 | -0.1286917 | -0.0807406 | -0.1231640 | -0.0459005 | -0.0007875 | -0.1463724 | -0.0409475 |
| PatientSurveyStarRating_H_QUIET_STAR_RATING | PatientSurveyStarRating_H_QUIET_STAR_RATING | -0.1047116 | -0.1748506 | -0.1621036 | 0.0177193 | 0.6176477 | 0.5958859 | 0.6138696 | 0.5922228 | 0.4292218 | 0.6101555 | 0.4987457 | 1.0000000 | 0.6313199 | 0.5470896 | 0.6730863 | 0.6317832 | 0.6199984 | 0.6414418 | 0.6171349 | 0.4395790 | 0.6249789 | 0.5123475 | 0.9556614 | 0.6537481 | 0.5755335 | -0.3375393 | -0.1460561 | 0.0866767 | -0.3615058 | -0.1742767 | -0.2095914 | 0.0018524 | -0.0044671 | 0.0303764 | 0.0919836 | 0.0246699 | -0.0291912 | 0.1003528 | 0.1010108 | 0.0765341 | 0.1069590 | 0.0440800 | -0.0897193 | -0.0754020 | -0.0250746 | 0.0428372 | 0.0722857 | 0.1510853 | 0.0902457 | 0.0337813 | -0.0457618 | -0.0343542 | -0.0062045 | 0.0093687 | 0.0220979 | -0.0051357 | -0.0772394 | -0.0730231 | -0.1033157 | -0.0277664 | -0.0213211 | -0.0977954 | -0.0612456 |
| PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | PatientSurveyStarRating_H_HSP_RATING_STAR_RATING | -0.1709175 | -0.2016141 | -0.1408372 | 0.1181224 | 0.8113195 | 0.7576397 | 0.7478412 | 0.7502954 | 0.6769019 | 0.8272215 | 0.5965227 | 0.6313199 | 1.0000000 | 0.8595636 | 0.8714961 | 0.8454781 | 0.7811879 | 0.7821354 | 0.7928741 | 0.7081668 | 0.8548308 | 0.6252851 | 0.6598602 | 0.9428150 | 0.9030319 | -0.3410053 | 0.0325646 | 0.2088800 | -0.2358015 | -0.1677838 | -0.2095003 | 0.0163640 | 0.0773879 | 0.1105834 | 0.1692958 | 0.0877507 | 0.0093791 | 0.1897215 | 0.1544748 | 0.2160677 | 0.1589966 | 0.2063053 | -0.0348377 | 0.0691782 | -0.0967251 | -0.0951559 | -0.0338034 | 0.0240132 | -0.0613852 | -0.0814552 | -0.0490180 | -0.0852573 | -0.0166476 | -0.0686735 | 0.0710883 | -0.0270735 | -0.1585705 | -0.0643414 | -0.1472927 | -0.0205437 | 0.0287970 | -0.1387904 | -0.2018848 |
| PatientSurveyStarRating_H_RECMND_STAR_RATING | PatientSurveyStarRating_H_RECMND_STAR_RATING | -0.1659007 | -0.1642097 | -0.0923845 | 0.1391159 | 0.7410800 | 0.7012869 | 0.6650712 | 0.6685334 | 0.6409371 | 0.7939928 | 0.5237951 | 0.5470896 | 0.8595636 | 1.0000000 | 0.7960527 | 0.7806696 | 0.7300827 | 0.6882451 | 0.7037121 | 0.6761773 | 0.8247493 | 0.5530915 | 0.5738169 | 0.9052988 | 0.9480759 | -0.2979673 | 0.0850940 | 0.2212547 | -0.1390380 | -0.1405352 | -0.1692993 | -0.0078546 | 0.0885267 | 0.1190673 | 0.1532363 | 0.0735792 | 0.0265116 | 0.1760486 | 0.1358664 | 0.2348521 | 0.1378362 | 0.2020601 | 0.0037855 | 0.0981665 | -0.1246042 | -0.0981817 | -0.0266626 | -0.0005694 | -0.1107731 | -0.1033645 | -0.0387585 | -0.0857042 | -0.0221487 | -0.0800058 | 0.0787681 | 0.0075443 | -0.1576927 | -0.0393015 | -0.1297623 | 0.0134380 | 0.0262619 | -0.1202753 | -0.2235957 |
| PatientSurveyStarRating_H_STAR_RATING | PatientSurveyStarRating_H_STAR_RATING | -0.1783759 | -0.2308937 | -0.1744075 | 0.0779986 | 0.8821074 | 0.8135436 | 0.8260329 | 0.8320006 | 0.7586740 | 0.8743494 | 0.6391671 | 0.6730863 | 0.8714961 | 0.7960527 | 1.0000000 | 0.8920184 | 0.8314222 | 0.8501602 | 0.8592370 | 0.7769593 | 0.8837377 | 0.6599688 | 0.6959526 | 0.8852220 | 0.8308877 | -0.3910783 | 0.0049679 | 0.2156832 | -0.3210235 | -0.2011786 | -0.2229136 | 0.0373120 | 0.0727680 | 0.1107188 | 0.1500361 | 0.0637937 | -0.0494590 | 0.1800467 | 0.1591593 | 0.1860190 | 0.1945060 | 0.1646616 | -0.0552836 | 0.0051809 | -0.0822439 | -0.0710775 | -0.0009117 | 0.0947303 | -0.0145406 | -0.0275058 | -0.0283146 | -0.0547617 | 0.0061083 | -0.0156145 | 0.0913137 | -0.0452172 | -0.1540619 | -0.0738343 | -0.1614933 | -0.0003415 | 0.0303266 | -0.1263561 | -0.1981220 |
| HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE | -0.1666746 | -0.2067264 | -0.1524571 | 0.0692166 | 0.9413741 | 0.7932584 | 0.8314522 | 0.8038535 | 0.7386381 | 0.8277780 | 0.5928460 | 0.6317832 | 0.8454781 | 0.7806696 | 0.8920184 | 1.0000000 | 0.8343982 | 0.8821498 | 0.8499295 | 0.7853207 | 0.8750498 | 0.6213823 | 0.6671546 | 0.8901557 | 0.8322887 | -0.3639238 | -0.0217481 | 0.2169674 | -0.3107282 | -0.1745392 | -0.2104045 | 0.0422069 | 0.0800014 | 0.1588429 | 0.1603505 | 0.0495956 | -0.0522724 | 0.1983566 | 0.1847906 | 0.2367758 | 0.2012691 | 0.2088467 | -0.0544304 | 0.0173084 | -0.0796721 | -0.0452192 | -0.0003548 | 0.1228267 | 0.0156651 | -0.0170135 | -0.0236955 | -0.0303324 | -0.0019546 | -0.0080622 | 0.0845264 | -0.0270956 | -0.1428877 | -0.0663743 | -0.1459851 | -0.0029199 | 0.0385911 | -0.1096342 | -0.2107792 |
| HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE | -0.1802262 | -0.2352248 | -0.1798454 | 0.0716989 | 0.7947199 | 0.9499147 | 0.7052054 | 0.7587947 | 0.6584544 | 0.7995932 | 0.5111389 | 0.6199984 | 0.7811879 | 0.7300827 | 0.8314222 | 0.8343982 | 1.0000000 | 0.7459134 | 0.8003061 | 0.6977774 | 0.8394911 | 0.5323986 | 0.6495609 | 0.8179081 | 0.7709408 | -0.3129324 | 0.0155923 | 0.2128270 | -0.2565866 | -0.1409829 | -0.1463552 | 0.0002792 | 0.0782762 | 0.0802578 | 0.1011092 | 0.0415646 | -0.0658150 | 0.1433814 | 0.0975333 | 0.1984598 | 0.1920243 | 0.1718119 | -0.0972506 | -0.0314520 | -0.0680571 | -0.0565823 | -0.0027657 | 0.0845920 | 0.0121242 | 0.0058744 | 0.0019106 | -0.0063422 | 0.0334780 | -0.0028885 | 0.0956884 | -0.0236698 | -0.1414366 | -0.0380979 | -0.1394736 | -0.0143285 | 0.0425664 | -0.0826332 | -0.1875503 |
| HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE | -0.1831908 | -0.2449572 | -0.1926175 | 0.0290342 | 0.8439662 | 0.7139678 | 0.9423558 | 0.7931167 | 0.6847212 | 0.7605870 | 0.6220272 | 0.6414418 | 0.7821354 | 0.6882451 | 0.8501602 | 0.8821498 | 0.7459134 | 1.0000000 | 0.8363904 | 0.7154682 | 0.7981273 | 0.6483447 | 0.6695835 | 0.8221836 | 0.7369761 | -0.3677548 | -0.0905698 | 0.1363775 | -0.4107053 | -0.2259603 | -0.2585483 | 0.0412981 | 0.0395669 | 0.0998162 | 0.1511649 | 0.0576566 | -0.0681526 | 0.1772872 | 0.1643799 | 0.1340445 | 0.1572709 | 0.1065507 | -0.1148325 | -0.0405641 | -0.0605862 | -0.0215130 | 0.0433887 | 0.1663036 | 0.0511012 | 0.0476323 | -0.0223315 | -0.0117228 | 0.0152153 | 0.0187552 | 0.0790559 | -0.0330533 | -0.1408040 | -0.0953948 | -0.1388091 | -0.0063199 | 0.0405341 | -0.1131291 | -0.1799323 |
| HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE | -0.1775098 | -0.2546502 | -0.2093859 | 0.0495607 | 0.8192660 | 0.7675074 | 0.7878299 | 0.9410632 | 0.7015421 | 0.8011319 | 0.5982789 | 0.6171349 | 0.7928741 | 0.7037121 | 0.8592370 | 0.8499295 | 0.8003061 | 0.8363904 | 1.0000000 | 0.7405838 | 0.8369260 | 0.6236211 | 0.6436063 | 0.8239488 | 0.7494949 | -0.3259347 | -0.0050286 | 0.1728899 | -0.3312654 | -0.1928398 | -0.1986827 | 0.0259892 | 0.0644512 | 0.0816216 | 0.1501584 | 0.0726417 | -0.0510706 | 0.1823598 | 0.1625916 | 0.1647114 | 0.2084062 | 0.1450104 | -0.1112139 | -0.0255467 | -0.0557335 | -0.0641464 | -0.0091794 | 0.0704411 | -0.0036037 | 0.0323273 | -0.0128473 | -0.0052418 | 0.0247052 | -0.0039459 | 0.0793646 | -0.0452983 | -0.1557561 | -0.0685223 | -0.1386420 | -0.0063967 | 0.0420468 | -0.1086247 | -0.1660056 |
| HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE | -0.1844376 | -0.2079982 | -0.1348924 | 0.0542992 | 0.7212981 | 0.6648163 | 0.6812130 | 0.6945471 | 0.9400388 | 0.7433629 | 0.4927072 | 0.4395790 | 0.7081668 | 0.6761773 | 0.7769593 | 0.7853207 | 0.6977774 | 0.7154682 | 0.7405838 | 1.0000000 | 0.7968806 | 0.5182906 | 0.4744589 | 0.7556492 | 0.7238004 | -0.2900150 | 0.0369924 | 0.2564050 | -0.2066316 | -0.1376094 | -0.1292560 | 0.0701172 | 0.1220570 | 0.1274513 | 0.1720468 | 0.0650279 | -0.0549999 | 0.2122738 | 0.2084118 | 0.2236306 | 0.2431733 | 0.2738285 | 0.0383041 | 0.0559072 | -0.1078990 | -0.0733482 | 0.0081764 | 0.1281267 | -0.0315360 | 0.0086356 | 0.0049870 | 0.0228481 | 0.0256121 | -0.0176853 | 0.0859133 | -0.0145875 | -0.1539039 | -0.0536226 | -0.1430776 | 0.0069610 | 0.0649921 | -0.0872152 | -0.2310210 |
| HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE | -0.1877798 | -0.2250397 | -0.1608376 | 0.1106351 | 0.8279021 | 0.8027744 | 0.7546488 | 0.7903879 | 0.7549683 | 0.9482189 | 0.5781200 | 0.6249789 | 0.8548308 | 0.8247493 | 0.8837377 | 0.8750498 | 0.8394911 | 0.7981273 | 0.8369260 | 0.7968806 | 1.0000000 | 0.6025716 | 0.6612420 | 0.9026621 | 0.8770384 | -0.3383196 | 0.0347599 | 0.2352166 | -0.2492489 | -0.1534787 | -0.2043225 | 0.0287005 | 0.0923400 | 0.1255794 | 0.1636239 | 0.0630912 | -0.0194129 | 0.2014907 | 0.1664733 | 0.2341396 | 0.1543470 | 0.2096414 | -0.0268317 | 0.0534239 | -0.1051209 | -0.1115171 | -0.0514076 | 0.0259241 | -0.0829950 | -0.0564195 | -0.0211932 | -0.0638071 | 0.0098029 | -0.0381375 | 0.0825062 | -0.0316508 | -0.1629600 | -0.0614646 | -0.1636005 | -0.0189605 | 0.0320446 | -0.1202132 | -0.2069970 |
| HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE | -0.1223047 | -0.2045779 | -0.1848471 | 0.0187289 | 0.6062342 | 0.5120334 | 0.6155860 | 0.5987567 | 0.4999730 | 0.5929605 | 0.9570846 | 0.5123475 | 0.6252851 | 0.5530915 | 0.6599688 | 0.6213823 | 0.5323986 | 0.6483447 | 0.6236211 | 0.5182906 | 0.6025716 | 1.0000000 | 0.5250997 | 0.6604779 | 0.5844106 | -0.3337655 | -0.0182565 | 0.0860619 | -0.3285392 | -0.1777808 | -0.2595566 | 0.0253586 | 0.0135852 | 0.0584827 | 0.1855312 | 0.1034169 | -0.0099818 | 0.2012726 | 0.1514801 | 0.0455756 | 0.1000540 | 0.0284015 | -0.0880614 | -0.0051760 | -0.0539829 | -0.0624749 | -0.0255442 | 0.0677998 | -0.0009274 | -0.0795298 | -0.0709592 | -0.1182074 | -0.0369162 | 0.0027541 | -0.0109302 | -0.0753999 | -0.1233582 | -0.0813748 | -0.1261494 | -0.0505286 | 0.0061946 | -0.1486299 | -0.0439537 |
| HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE | -0.1123794 | -0.1801665 | -0.1649195 | 0.0248142 | 0.6396807 | 0.6187305 | 0.6319284 | 0.6129310 | 0.4567781 | 0.6371231 | 0.5105668 | 0.9556614 | 0.6598602 | 0.5738169 | 0.6959526 | 0.6671546 | 0.6495609 | 0.6695835 | 0.6436063 | 0.4744589 | 0.6612420 | 0.5250997 | 1.0000000 | 0.6896579 | 0.6107149 | -0.3437480 | -0.1442006 | 0.1064024 | -0.3661649 | -0.1699262 | -0.2145544 | -0.0022965 | 0.0082056 | 0.0494650 | 0.0908135 | 0.0158871 | -0.0303120 | 0.1023846 | 0.1022135 | 0.0817038 | 0.1098322 | 0.0437557 | -0.0886019 | -0.0634120 | -0.0341816 | 0.0366437 | 0.0773892 | 0.1553155 | 0.0950135 | 0.0255069 | -0.0454692 | -0.0450913 | -0.0062861 | 0.0051695 | 0.0262747 | -0.0054371 | -0.0789391 | -0.0813685 | -0.1096072 | -0.0256155 | -0.0196124 | -0.1003157 | -0.0653730 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | -0.1779322 | -0.2060912 | -0.1425272 | 0.1116353 | 0.8361084 | 0.7826816 | 0.7774308 | 0.7793734 | 0.7098506 | 0.8571093 | 0.6248221 | 0.6537481 | 0.9428150 | 0.9052988 | 0.8852220 | 0.8901557 | 0.8179081 | 0.8221836 | 0.8239488 | 0.7556492 | 0.9026621 | 0.6604779 | 0.6896579 | 1.0000000 | 0.9580767 | -0.3521196 | 0.0302154 | 0.2182505 | -0.2448842 | -0.1752428 | -0.2290270 | 0.0077229 | 0.0865028 | 0.1100002 | 0.1826272 | 0.0844502 | 0.0072643 | 0.2115650 | 0.1691943 | 0.2211843 | 0.1738457 | 0.2315523 | -0.0248375 | 0.0775236 | -0.1091775 | -0.0952111 | -0.0230632 | 0.0300940 | -0.0702915 | -0.0760295 | -0.0499063 | -0.0948401 | -0.0054742 | -0.0590998 | 0.0707774 | -0.0157615 | -0.1615833 | -0.0674588 | -0.1458284 | -0.0172231 | 0.0304634 | -0.1390570 | -0.2108956 |
| HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE | -0.1852873 | -0.1911254 | -0.1150026 | 0.1516789 | 0.7761632 | 0.7365285 | 0.6961148 | 0.7066986 | 0.6770439 | 0.8310741 | 0.5505967 | 0.5755335 | 0.9030319 | 0.9480759 | 0.8308877 | 0.8322887 | 0.7709408 | 0.7369761 | 0.7494949 | 0.7238004 | 0.8770384 | 0.5844106 | 0.6107149 | 0.9580767 | 1.0000000 | -0.2951331 | 0.0831560 | 0.2197432 | -0.1585545 | -0.1471003 | -0.1980854 | -0.0179814 | 0.0967347 | 0.1229267 | 0.1676529 | 0.0687394 | 0.0292287 | 0.1933820 | 0.1501468 | 0.2439658 | 0.1443141 | 0.2442083 | 0.0058311 | 0.1184210 | -0.1332048 | -0.1095716 | -0.0262870 | -0.0003806 | -0.1100101 | -0.1074875 | -0.0363290 | -0.1008569 | -0.0193730 | -0.0826240 | 0.0862428 | -0.0105068 | -0.1710237 | -0.0510987 | -0.1364494 | 0.0003983 | 0.0331751 | -0.1279752 | -0.2364653 |
| ED_2_Strata_1 | ED_2_Strata_1 | 0.0762327 | 0.1082272 | 0.0843876 | 0.0425263 | -0.3811431 | -0.2976284 | -0.3896187 | -0.3575914 | -0.3008610 | -0.3553994 | -0.3195008 | -0.3375393 | -0.3410053 | -0.2979673 | -0.3910783 | -0.3639238 | -0.3129324 | -0.3677548 | -0.3259347 | -0.2900150 | -0.3383196 | -0.3337655 | -0.3437480 | -0.3521196 | -0.2951331 | 1.0000000 | 0.1248128 | 0.0186099 | 0.5775206 | 0.4204676 | 0.3419958 | -0.0684821 | 0.0164159 | -0.0486040 | -0.1152627 | -0.0615764 | -0.0354024 | -0.1265965 | -0.0558505 | -0.0983294 | 0.0254940 | -0.0925059 | 0.0761139 | 0.0321584 | 0.0673396 | 0.0579101 | -0.0537002 | -0.1136600 | -0.0389080 | 0.0005912 | 0.0602674 | -0.0541503 | 0.0551291 | -0.0211509 | -0.0336614 | 0.0315830 | 0.1430203 | 0.1038617 | 0.0817412 | 0.0633107 | -0.0393782 | 0.1286484 | 0.1212071 |
| HCP_COVID_19 | HCP_COVID_19 | -0.0508152 | -0.0563082 | -0.0316503 | 0.0450149 | -0.0217341 | 0.0206259 | -0.0747053 | -0.0062821 | 0.0354071 | 0.0397427 | -0.0225176 | -0.1460561 | 0.0325646 | 0.0850940 | 0.0049679 | -0.0217481 | 0.0155923 | -0.0905698 | -0.0050286 | 0.0369924 | 0.0347599 | -0.0182565 | -0.1442006 | 0.0302154 | 0.0831560 | 0.1248128 | 1.0000000 | 0.3203622 | 0.2574291 | 0.0698819 | 0.1122982 | -0.0306155 | 0.1067941 | -0.0812735 | -0.0345104 | 0.0310392 | -0.0124470 | -0.0175650 | -0.1149175 | 0.0947908 | 0.0304334 | 0.0831624 | 0.0241622 | -0.0151698 | -0.0510683 | -0.0869890 | -0.1128278 | -0.1245435 | -0.1523779 | -0.0988833 | 0.0943953 | 0.0417007 | 0.0225916 | -0.0272232 | 0.0549990 | 0.0009222 | -0.0909811 | 0.1091949 | -0.0160408 | 0.0169512 | 0.0430812 | 0.0534106 | -0.0627505 |
| IMM_3 | IMM_3 | -0.0583326 | -0.0028840 | 0.0435410 | 0.0255192 | 0.1991033 | 0.2149395 | 0.1498815 | 0.1693415 | 0.2404893 | 0.2374707 | 0.0814169 | 0.0866767 | 0.2088800 | 0.2212547 | 0.2156832 | 0.2169674 | 0.2128270 | 0.1363775 | 0.1728899 | 0.2564050 | 0.2352166 | 0.0860619 | 0.1064024 | 0.2182505 | 0.2197432 | 0.0186099 | 0.3203622 | 1.0000000 | 0.1105628 | 0.0343661 | 0.0560372 | 0.0400235 | 0.1317922 | 0.0410289 | 0.0439297 | 0.0519538 | -0.0201631 | 0.0361783 | 0.0484998 | 0.1318005 | 0.1020708 | 0.0900367 | 0.0906329 | 0.0058144 | -0.0212916 | -0.0165321 | -0.0616051 | -0.0010634 | -0.0761104 | 0.0146397 | 0.0451508 | 0.0601831 | 0.0544576 | -0.0311579 | 0.0899361 | 0.0412713 | -0.0625676 | 0.0594933 | -0.0250714 | 0.0723872 | 0.0625753 | 0.0226639 | -0.0720431 |
| OP_18b | OP_18b | 0.0505288 | 0.1295727 | 0.1403298 | 0.0895220 | -0.3200784 | -0.2473453 | -0.3981251 | -0.3234673 | -0.2071783 | -0.2572208 | -0.3267508 | -0.3615058 | -0.2358015 | -0.1390380 | -0.3210235 | -0.3107282 | -0.2565866 | -0.4107053 | -0.3312654 | -0.2066316 | -0.2492489 | -0.3285392 | -0.3661649 | -0.2448842 | -0.1585545 | 0.5775206 | 0.2574291 | 0.1105628 | 1.0000000 | 0.4959758 | 0.5894838 | -0.0756249 | 0.0506067 | -0.1400845 | -0.1714513 | -0.0557159 | 0.0160417 | -0.2007893 | -0.1593730 | 0.0723310 | -0.1190028 | 0.0948584 | 0.2344307 | 0.0629824 | -0.0293698 | -0.0678837 | -0.1527290 | -0.2195933 | -0.1858291 | -0.0905644 | 0.0583644 | 0.0638412 | 0.0187794 | -0.0806516 | 0.0224833 | 0.0544528 | -0.0076797 | 0.1653812 | 0.0993570 | 0.0676972 | 0.0374530 | 0.0986077 | -0.0241965 |
| OP_18c | OP_18c | 0.0838395 | 0.0877808 | 0.0491515 | 0.0668517 | -0.1816118 | -0.1452767 | -0.2241446 | -0.1918556 | -0.1351116 | -0.1626711 | -0.1804160 | -0.1742767 | -0.1677838 | -0.1405352 | -0.2011786 | -0.1745392 | -0.1409829 | -0.2259603 | -0.1928398 | -0.1376094 | -0.1534787 | -0.1777808 | -0.1699262 | -0.1752428 | -0.1471003 | 0.4204676 | 0.0698819 | 0.0343661 | 0.4959758 | 1.0000000 | 0.3393524 | 0.0090382 | 0.0430774 | -0.0573485 | -0.0726374 | -0.0430698 | 0.0018369 | -0.0844129 | -0.0481731 | -0.0076033 | -0.0476864 | 0.0158359 | 0.1235925 | 0.0408574 | 0.0028501 | -0.0296718 | -0.0977614 | -0.1375348 | -0.0578145 | -0.0463395 | 0.0181970 | 0.0046272 | 0.0392571 | -0.0457654 | -0.0128057 | 0.0031479 | 0.0074122 | 0.0486693 | 0.0559662 | 0.0192234 | 0.0049833 | 0.0401196 | 0.0264108 |
| OP_22 | OP_22 | 0.0252830 | 0.0501727 | 0.0510729 | 0.0478896 | -0.2152685 | -0.1462753 | -0.2360570 | -0.1939137 | -0.1225568 | -0.2149719 | -0.2479612 | -0.2095914 | -0.2095003 | -0.1692993 | -0.2229136 | -0.2104045 | -0.1463552 | -0.2585483 | -0.1986827 | -0.1292560 | -0.2043225 | -0.2595566 | -0.2145544 | -0.2290270 | -0.1980854 | 0.3419958 | 0.1122982 | 0.0560372 | 0.5894838 | 0.3393524 | 1.0000000 | -0.0949210 | 0.0293870 | -0.1014242 | -0.2178259 | -0.1066885 | -0.0990718 | -0.2279495 | -0.1444805 | -0.0066518 | -0.0291353 | 0.0057888 | 0.0805708 | -0.0317460 | 0.0214909 | 0.0376811 | -0.0446228 | -0.0767863 | -0.0559254 | 0.0198257 | 0.0648778 | 0.1019307 | 0.0452860 | 0.0102293 | 0.0426036 | 0.0343100 | 0.0397075 | 0.0778917 | 0.0451515 | 0.0652307 | 0.0094012 | 0.0926476 | -0.0237710 |
| OP_23 | OP_23 | 0.0485500 | 0.0614226 | 0.0366382 | -0.0553207 | 0.0440556 | 0.0003840 | 0.0486530 | 0.0438526 | 0.0625152 | 0.0359131 | 0.0190913 | 0.0018524 | 0.0163640 | -0.0078546 | 0.0373120 | 0.0422069 | 0.0002792 | 0.0412981 | 0.0259892 | 0.0701172 | 0.0287005 | 0.0253586 | -0.0022965 | 0.0077229 | -0.0179814 | -0.0684821 | -0.0306155 | 0.0400235 | -0.0756249 | 0.0090382 | -0.0949210 | 1.0000000 | 0.0732988 | 0.0371036 | 0.1919253 | 0.1198314 | 0.0629739 | 0.1834131 | 0.1440580 | 0.0821585 | 0.1352184 | 0.0401311 | 0.2485615 | 0.1664553 | 0.0398944 | 0.0034495 | 0.0135325 | 0.0045056 | -0.0210473 | -0.0614632 | -0.0570477 | -0.0326986 | -0.0515403 | 0.0269822 | -0.0140788 | -0.0045339 | 0.0279871 | -0.0606628 | -0.0384416 | -0.0593032 | -0.0270588 | -0.0530033 | 0.0545394 |
| OP_29 | OP_29 | -0.0296026 | -0.0106510 | 0.0098417 | -0.0126112 | 0.0700383 | 0.0721589 | 0.0507759 | 0.0664748 | 0.1045421 | 0.0858822 | 0.0137026 | -0.0044671 | 0.0773879 | 0.0885267 | 0.0727680 | 0.0800014 | 0.0782762 | 0.0395669 | 0.0644512 | 0.1220570 | 0.0923400 | 0.0135852 | 0.0082056 | 0.0865028 | 0.0967347 | 0.0164159 | 0.1067941 | 0.1317922 | 0.0506067 | 0.0430774 | 0.0293870 | 0.0732988 | 1.0000000 | -0.0650231 | 0.0952846 | 0.0685334 | 0.0455411 | 0.1111026 | 0.0304354 | 0.0653732 | 0.0263917 | 0.0285800 | 0.1567526 | 0.0271825 | -0.0096464 | -0.0600569 | 0.0081252 | 0.0099705 | -0.0536184 | -0.0251654 | -0.0032584 | 0.0312780 | 0.0059688 | -0.0199006 | 0.0150699 | 0.0331569 | -0.0837910 | -0.0108546 | -0.0087678 | 0.0163779 | 0.0419068 | -0.0312757 | -0.0815209 |
| SAFE_USE_OF_OPIOIDS | SAFE_USE_OF_OPIOIDS | 0.0627005 | 0.1063002 | 0.0946020 | 0.0749623 | 0.1401327 | 0.0759631 | 0.0900327 | 0.0784938 | 0.1184473 | 0.1201701 | 0.0598688 | 0.0303764 | 0.1105834 | 0.1190673 | 0.1107188 | 0.1588429 | 0.0802578 | 0.0998162 | 0.0816216 | 0.1274513 | 0.1255794 | 0.0584827 | 0.0494650 | 0.1100002 | 0.1229267 | -0.0486040 | -0.0812735 | 0.0410289 | -0.1400845 | -0.0573485 | -0.1014242 | 0.0371036 | -0.0650231 | 1.0000000 | 0.0650110 | 0.0100193 | 0.0697614 | 0.0854069 | 0.0857646 | 0.1450732 | 0.0858291 | 0.1347354 | -0.0563373 | 0.1913961 | -0.0081923 | -0.0643353 | -0.0362573 | 0.0171605 | -0.0204107 | -0.0804707 | -0.0287158 | -0.0995591 | -0.0603629 | -0.0017558 | -0.0043396 | -0.0382369 | 0.0137283 | -0.0380145 | -0.0258097 | -0.0166146 | -0.0268805 | -0.0300979 | -0.0048449 |
| SEP_1 | SEP_1 | -0.0309171 | -0.0326387 | -0.0189720 | 0.0004693 | 0.1288469 | 0.0920878 | 0.1321630 | 0.1324347 | 0.1443955 | 0.1393844 | 0.1690161 | 0.0919836 | 0.1692958 | 0.1532363 | 0.1500361 | 0.1603505 | 0.1011092 | 0.1511649 | 0.1501584 | 0.1720468 | 0.1636239 | 0.1855312 | 0.0908135 | 0.1826272 | 0.1676529 | -0.1152627 | -0.0345104 | 0.0439297 | -0.1714513 | -0.0726374 | -0.2178259 | 0.1919253 | 0.0952846 | 0.0650110 | 1.0000000 | 0.7309445 | 0.5744973 | 0.8329106 | 0.6460143 | 0.0976908 | 0.0923975 | 0.1245526 | 0.2303117 | 0.2029424 | -0.0347567 | -0.0154393 | 0.0197865 | 0.0170609 | -0.0335808 | -0.0769699 | -0.0797702 | -0.0937443 | -0.0339321 | -0.0179990 | -0.0245784 | -0.0390884 | -0.0564170 | -0.0757690 | -0.0254596 | -0.0644595 | -0.0051105 | -0.1034135 | -0.0078309 |
| SEP_SH_3HR | SEP_SH_3HR | -0.0529663 | -0.0689850 | -0.0503969 | -0.0353431 | 0.0438649 | 0.0418658 | 0.0521192 | 0.0587805 | 0.0557260 | 0.0474753 | 0.0858056 | 0.0246699 | 0.0877507 | 0.0735792 | 0.0637937 | 0.0495956 | 0.0415646 | 0.0576566 | 0.0726417 | 0.0650279 | 0.0630912 | 0.1034169 | 0.0158871 | 0.0844502 | 0.0687394 | -0.0615764 | 0.0310392 | 0.0519538 | -0.0557159 | -0.0430698 | -0.1066885 | 0.1198314 | 0.0685334 | 0.0100193 | 0.7309445 | 1.0000000 | 0.3894182 | 0.5124276 | 0.3029233 | 0.0214458 | -0.0003039 | 0.0083388 | 0.0443562 | 0.0597759 | -0.0068112 | 0.0143010 | 0.0121194 | 0.0396301 | 0.0312695 | -0.0311801 | -0.0131473 | 0.0329404 | 0.0208887 | -0.0022962 | 0.0293689 | -0.0176156 | -0.0367514 | -0.0072258 | 0.0030403 | -0.0148982 | 0.0376698 | -0.0243705 | 0.0044851 |
| SEP_SH_6HR | SEP_SH_6HR | 0.0313734 | 0.0231777 | 0.0048063 | 0.0408255 | -0.0683424 | -0.0697479 | -0.0725481 | -0.0562912 | -0.0637431 | -0.0250525 | -0.0192334 | -0.0291912 | 0.0093791 | 0.0265116 | -0.0494590 | -0.0522724 | -0.0658150 | -0.0681526 | -0.0510706 | -0.0549999 | -0.0194129 | -0.0099818 | -0.0303120 | 0.0072643 | 0.0292287 | -0.0354024 | -0.0124470 | -0.0201631 | 0.0160417 | 0.0018369 | -0.0990718 | 0.0629739 | 0.0455411 | 0.0697614 | 0.5744973 | 0.3894182 | 1.0000000 | 0.3849719 | 0.2338399 | -0.0161607 | -0.0334702 | -0.0156808 | 0.1359384 | 0.1422354 | -0.0041075 | -0.0631275 | -0.0279555 | -0.0515598 | -0.0787945 | -0.0854897 | -0.0659226 | -0.0727333 | -0.0738364 | -0.0368684 | -0.0345921 | 0.0185638 | -0.0310002 | -0.0467304 | -0.0030434 | -0.0228048 | 0.0032891 | -0.0713756 | 0.0072647 |
| SEV_SEP_3HR | SEV_SEP_3HR | -0.0322822 | -0.0375511 | -0.0227213 | 0.0074947 | 0.1642083 | 0.1295914 | 0.1605381 | 0.1606626 | 0.1782560 | 0.1732976 | 0.1869822 | 0.1003528 | 0.1897215 | 0.1760486 | 0.1800467 | 0.1983566 | 0.1433814 | 0.1772872 | 0.1823598 | 0.2122738 | 0.2014907 | 0.2012726 | 0.1023846 | 0.2115650 | 0.1933820 | -0.1265965 | -0.0175650 | 0.0361783 | -0.2007893 | -0.0844129 | -0.2279495 | 0.1834131 | 0.1111026 | 0.0854069 | 0.8329106 | 0.5124276 | 0.3849719 | 1.0000000 | 0.4694083 | 0.1935342 | 0.1476882 | 0.2318112 | 0.2111767 | 0.2098349 | -0.0375621 | -0.0091369 | 0.0251530 | 0.0425397 | -0.0246311 | -0.0663090 | -0.0624234 | -0.0956269 | -0.0378140 | -0.0141213 | -0.0257851 | -0.0434036 | -0.0699820 | -0.0642500 | -0.0485933 | -0.0700502 | -0.0047427 | -0.0993958 | -0.0154257 |
| SEV_SEP_6HR | SEV_SEP_6HR | -0.0278551 | -0.0014846 | 0.0212143 | 0.0166986 | 0.1503950 | 0.0899079 | 0.1368127 | 0.1423359 | 0.1758781 | 0.1422983 | 0.1376097 | 0.1010108 | 0.1544748 | 0.1358664 | 0.1591593 | 0.1847906 | 0.0975333 | 0.1643799 | 0.1625916 | 0.2084118 | 0.1664733 | 0.1514801 | 0.1022135 | 0.1691943 | 0.1501468 | -0.0558505 | -0.1149175 | 0.0484998 | -0.1593730 | -0.0481731 | -0.1444805 | 0.1440580 | 0.0304354 | 0.0857646 | 0.6460143 | 0.3029233 | 0.2338399 | 0.4694083 | 1.0000000 | 0.0037384 | 0.0649257 | 0.0631441 | 0.2522518 | 0.2083065 | -0.0109117 | -0.0072140 | 0.0299786 | 0.0287674 | -0.0152374 | -0.0457369 | -0.0572546 | -0.0824732 | -0.0074196 | -0.0351047 | -0.0587674 | -0.0265019 | -0.0400763 | -0.0886155 | -0.0494623 | -0.0377753 | -0.0179532 | -0.0875069 | -0.0092648 |
| STK_02 | STK_02 | -0.0434539 | 0.0066866 | 0.0415201 | 0.0343487 | 0.2176041 | 0.1940882 | 0.1201354 | 0.1358181 | 0.2091327 | 0.2157600 | 0.0367533 | 0.0765341 | 0.2160677 | 0.2348521 | 0.1860190 | 0.2367758 | 0.1984598 | 0.1340445 | 0.1647114 | 0.2236306 | 0.2341396 | 0.0455756 | 0.0817038 | 0.2211843 | 0.2439658 | -0.0983294 | 0.0947908 | 0.1318005 | 0.0723310 | -0.0076033 | -0.0066518 | 0.0821585 | 0.0653732 | 0.1450732 | 0.0976908 | 0.0214458 | -0.0161607 | 0.1935342 | 0.0037384 | 1.0000000 | 0.3266488 | 0.8021725 | 0.4631419 | 0.4148180 | -0.0319671 | -0.0518587 | -0.0534076 | 0.0093937 | -0.0939059 | -0.0905922 | -0.0044494 | -0.0746093 | 0.0129386 | -0.0292526 | 0.0219699 | -0.0138235 | -0.0675289 | 0.0183344 | -0.0210595 | 0.0392025 | -0.0065909 | -0.0294786 | -0.1317922 |
| STK_05 | STK_05 | -0.0205857 | -0.0801055 | -0.0867129 | -0.0087498 | 0.1753654 | 0.1763581 | 0.1413712 | 0.1964279 | 0.2034132 | 0.1380616 | 0.0909053 | 0.1069590 | 0.1589966 | 0.1378362 | 0.1945060 | 0.2012691 | 0.1920243 | 0.1572709 | 0.2084062 | 0.2431733 | 0.1543470 | 0.1000540 | 0.1098322 | 0.1738457 | 0.1443141 | 0.0254940 | 0.0304334 | 0.1020708 | -0.1190028 | -0.0476864 | -0.0291353 | 0.1352184 | 0.0263917 | 0.0858291 | 0.0923975 | -0.0003039 | -0.0334702 | 0.1476882 | 0.0649257 | 0.3266488 | 1.0000000 | 0.2500620 | 0.5880079 | 0.6976272 | 0.0512645 | 0.0331009 | -0.0155739 | 0.1007144 | -0.0037918 | -0.0246261 | 0.0132861 | -0.0270607 | -0.0072465 | 0.0776413 | 0.0066697 | -0.0467887 | -0.0263715 | -0.0426109 | -0.0270503 | 0.0071556 | 0.0285160 | -0.0154089 | -0.0328044 |
| STK_06 | STK_06 | -0.0425563 | -0.0101158 | 0.0212012 | 0.0347316 | 0.1870897 | 0.1579604 | 0.0905514 | 0.1045092 | 0.1930126 | 0.1802407 | 0.0153703 | 0.0440800 | 0.2063053 | 0.2020601 | 0.1646616 | 0.2088467 | 0.1718119 | 0.1065507 | 0.1450104 | 0.2738285 | 0.2096414 | 0.0284015 | 0.0437557 | 0.2315523 | 0.2442083 | -0.0925059 | 0.0831624 | 0.0900367 | 0.0948584 | 0.0158359 | 0.0057888 | 0.0401311 | 0.0285800 | 0.1347354 | 0.1245526 | 0.0083388 | -0.0156808 | 0.2318112 | 0.0631441 | 0.8021725 | 0.2500620 | 1.0000000 | 0.4774227 | 0.5132722 | -0.0207810 | -0.0497188 | -0.0477381 | -0.0086007 | -0.0990850 | -0.0787235 | -0.0203047 | -0.0479955 | -0.0035153 | -0.0646189 | 0.0144757 | -0.0298356 | -0.0454564 | 0.0343065 | -0.0293121 | 0.0542351 | 0.0085622 | -0.0284310 | -0.1296356 |
| VTE_1 | VTE_1 | 0.0039309 | 0.0654668 | 0.0866767 | 0.0586463 | -0.0859359 | -0.0887946 | -0.0996751 | -0.0999738 | 0.0235381 | -0.0364693 | -0.0848392 | -0.0897193 | -0.0348377 | 0.0037855 | -0.0552836 | -0.0544304 | -0.0972506 | -0.1148325 | -0.1112139 | 0.0383041 | -0.0268317 | -0.0880614 | -0.0886019 | -0.0248375 | 0.0058311 | 0.0761139 | 0.0241622 | 0.0906329 | 0.2344307 | 0.1235925 | 0.0805708 | 0.2485615 | 0.1567526 | -0.0563373 | 0.2303117 | 0.0443562 | 0.1359384 | 0.2111767 | 0.2522518 | 0.4631419 | 0.5880079 | 0.4774227 | 1.0000000 | 0.8736490 | -0.0526925 | -0.0493931 | -0.0282911 | -0.1051171 | -0.1710235 | -0.1238916 | -0.0378500 | -0.1180160 | -0.0262166 | -0.0522876 | -0.0577213 | -0.0037803 | -0.0301775 | -0.0317534 | -0.0440578 | 0.0086141 | 0.0326658 | -0.0475558 | -0.1256363 |
| VTE_2 | VTE_2 | 0.0707936 | 0.1064994 | 0.0781368 | 0.0734882 | -0.0029321 | -0.0231504 | -0.0477865 | -0.0151359 | 0.0288319 | 0.0393084 | -0.0027352 | -0.0754020 | 0.0691782 | 0.0981665 | 0.0051809 | 0.0173084 | -0.0314520 | -0.0405641 | -0.0255467 | 0.0559072 | 0.0534239 | -0.0051760 | -0.0634120 | 0.0775236 | 0.1184210 | 0.0321584 | -0.0151698 | 0.0058144 | 0.0629824 | 0.0408574 | -0.0317460 | 0.1664553 | 0.0271825 | 0.1913961 | 0.2029424 | 0.0597759 | 0.1422354 | 0.2098349 | 0.2083065 | 0.4148180 | 0.6976272 | 0.5132722 | 0.8736490 | 1.0000000 | -0.0073599 | -0.0995881 | -0.1188383 | -0.1492708 | -0.1770482 | -0.1181448 | -0.0713006 | -0.1762469 | -0.0469516 | -0.1120007 | -0.0691325 | 0.0036343 | -0.0247178 | -0.0036828 | 0.0009109 | 0.0110118 | -0.0279567 | -0.0666219 | 0.0217206 |
| Score_COMP_HIP_KNEE | Score_COMP_HIP_KNEE | 0.4350513 | 0.3208550 | 0.0742579 | -0.1517362 | -0.0761301 | -0.0615070 | -0.0405313 | -0.0450744 | -0.0990420 | -0.1067242 | -0.0571772 | -0.0250746 | -0.0967251 | -0.1246042 | -0.0822439 | -0.0796721 | -0.0680571 | -0.0605862 | -0.0557335 | -0.1078990 | -0.1051209 | -0.0539829 | -0.0341816 | -0.1091775 | -0.1332048 | 0.0673396 | -0.0510683 | -0.0212916 | -0.0293698 | 0.0028501 | 0.0214909 | 0.0398944 | -0.0096464 | -0.0081923 | -0.0347567 | -0.0068112 | -0.0041075 | -0.0375621 | -0.0109117 | -0.0319671 | 0.0512645 | -0.0207810 | -0.0526925 | -0.0073599 | 1.0000000 | 0.0830479 | -0.0203930 | -0.0007242 | 0.0241066 | 0.0211621 | 0.0498557 | 0.0038509 | 0.0505415 | 0.0577776 | 0.0540124 | 0.0813038 | 0.1279724 | 0.1458258 | 0.1334619 | 0.0498603 | 0.0433809 | 0.1604802 | 0.3410864 |
| Score_MORT_30_AMI | Score_MORT_30_AMI | 0.0392653 | 0.0074065 | -0.0287661 | -0.1650703 | -0.0479940 | -0.0650654 | -0.0177776 | -0.0510480 | -0.0682603 | -0.1098730 | -0.0630079 | 0.0428372 | -0.0951559 | -0.0981817 | -0.0710775 | -0.0452192 | -0.0565823 | -0.0215130 | -0.0641464 | -0.0733482 | -0.1115171 | -0.0624749 | 0.0366437 | -0.0952111 | -0.1095716 | 0.0579101 | -0.0869890 | -0.0165321 | -0.0678837 | -0.0296718 | 0.0376811 | 0.0034495 | -0.0600569 | -0.0643353 | -0.0154393 | 0.0143010 | -0.0631275 | -0.0091369 | -0.0072140 | -0.0518587 | 0.0331009 | -0.0497188 | -0.0493931 | -0.0995881 | 0.0830479 | 1.0000000 | 0.2498600 | 0.3407616 | 0.3309425 | 0.2222539 | 0.0415523 | 0.2105379 | 0.0885083 | 0.1010348 | 0.0889343 | 0.1066619 | 0.1037006 | 0.0492328 | 0.0467554 | 0.0454462 | 0.0297688 | 0.1129695 | 0.0591548 |
| Score_MORT_30_COPD | Score_MORT_30_COPD | -0.0452304 | -0.0794948 | -0.0683850 | -0.0843669 | -0.0124762 | -0.0163367 | 0.0302290 | -0.0130589 | 0.0034724 | -0.0673445 | -0.0352455 | 0.0722857 | -0.0338034 | -0.0266626 | -0.0009117 | -0.0003548 | -0.0027657 | 0.0433887 | -0.0091794 | 0.0081764 | -0.0514076 | -0.0255442 | 0.0773892 | -0.0230632 | -0.0262870 | -0.0537002 | -0.1128278 | -0.0616051 | -0.1527290 | -0.0977614 | -0.0446228 | 0.0135325 | 0.0081252 | -0.0362573 | 0.0197865 | 0.0121194 | -0.0279555 | 0.0251530 | 0.0299786 | -0.0534076 | -0.0155739 | -0.0477381 | -0.0282911 | -0.1188383 | -0.0203930 | 0.2498600 | 1.0000000 | 0.3844105 | 0.3710744 | 0.2038243 | -0.0069743 | 0.1713379 | 0.0478268 | 0.0397571 | 0.0429090 | 0.0320669 | 0.0426574 | -0.0532586 | 0.0026944 | 0.0734846 | 0.0340007 | 0.0140214 | -0.0406696 |
| Score_MORT_30_HF | Score_MORT_30_HF | -0.0577013 | -0.1067828 | -0.0967183 | -0.1375901 | 0.1078488 | 0.0722015 | 0.1568119 | 0.0644025 | 0.1194712 | 0.0151872 | 0.0665845 | 0.1510853 | 0.0240132 | -0.0005694 | 0.0947303 | 0.1228267 | 0.0845920 | 0.1663036 | 0.0704411 | 0.1281267 | 0.0259241 | 0.0677998 | 0.1553155 | 0.0300940 | -0.0003806 | -0.1136600 | -0.1245435 | -0.0010634 | -0.2195933 | -0.1375348 | -0.0767863 | 0.0045056 | 0.0099705 | 0.0171605 | 0.0170609 | 0.0396301 | -0.0515598 | 0.0425397 | 0.0287674 | 0.0093937 | 0.1007144 | -0.0086007 | -0.1051171 | -0.1492708 | -0.0007242 | 0.3407616 | 0.3844105 | 1.0000000 | 0.4479367 | 0.3147371 | 0.0371596 | 0.2556384 | 0.0679149 | 0.1051698 | 0.0707269 | 0.0383771 | 0.0362529 | -0.0300702 | -0.0086832 | 0.0647245 | 0.0342374 | 0.0465081 | -0.0350247 |
| Score_MORT_30_PN | Score_MORT_30_PN | -0.0046351 | -0.0985660 | -0.1319780 | -0.1231721 | 0.0183801 | -0.0021513 | 0.0384967 | -0.0046993 | -0.0343594 | -0.0880905 | 0.0009844 | 0.0902457 | -0.0613852 | -0.1107731 | -0.0145406 | 0.0156651 | 0.0121242 | 0.0511012 | -0.0036037 | -0.0315360 | -0.0829950 | -0.0009274 | 0.0950135 | -0.0702915 | -0.1100101 | -0.0389080 | -0.1523779 | -0.0761104 | -0.1858291 | -0.0578145 | -0.0559254 | -0.0210473 | -0.0536184 | -0.0204107 | -0.0335808 | 0.0312695 | -0.0787945 | -0.0246311 | -0.0152374 | -0.0939059 | -0.0037918 | -0.0990850 | -0.1710235 | -0.1770482 | 0.0241066 | 0.3309425 | 0.3710744 | 0.4479367 | 1.0000000 | 0.3042563 | 0.0303815 | 0.2301195 | 0.0543554 | 0.0884315 | 0.0217880 | 0.0237048 | 0.0704445 | 0.0089560 | 0.0393676 | 0.0464407 | 0.0029691 | 0.0661595 | -0.0062985 |
| Score_MORT_30_STK | Score_MORT_30_STK | -0.0025521 | -0.0376746 | -0.0478311 | -0.1345921 | -0.0132887 | -0.0165143 | 0.0435642 | 0.0257903 | 0.0159051 | -0.0653713 | -0.0651509 | 0.0337813 | -0.0814552 | -0.1033645 | -0.0275058 | -0.0170135 | 0.0058744 | 0.0476323 | 0.0323273 | 0.0086356 | -0.0564195 | -0.0795298 | 0.0255069 | -0.0760295 | -0.1074875 | 0.0005912 | -0.0988833 | 0.0146397 | -0.0905644 | -0.0463395 | 0.0198257 | -0.0614632 | -0.0251654 | -0.0804707 | -0.0769699 | -0.0311801 | -0.0854897 | -0.0663090 | -0.0457369 | -0.0905922 | -0.0246261 | -0.0787235 | -0.1238916 | -0.1181448 | 0.0211621 | 0.2222539 | 0.2038243 | 0.3147371 | 0.3042563 | 1.0000000 | 0.0687216 | 0.2380935 | 0.0878847 | 0.1014879 | 0.0674377 | 0.0622532 | 0.0725381 | 0.0474896 | 0.0513975 | 0.0492194 | 0.0625191 | 0.1142992 | -0.0272101 |
| Score_PSI_03 | Score_PSI_03 | -0.0058883 | -0.0037334 | 0.0027232 | -0.0339593 | -0.0219430 | -0.0016914 | -0.0192053 | -0.0221085 | 0.0012755 | -0.0300329 | -0.0629368 | -0.0457618 | -0.0490180 | -0.0387585 | -0.0283146 | -0.0236955 | 0.0019106 | -0.0223315 | -0.0128473 | 0.0049870 | -0.0211932 | -0.0709592 | -0.0454692 | -0.0499063 | -0.0363290 | 0.0602674 | 0.0943953 | 0.0451508 | 0.0583644 | 0.0181970 | 0.0648778 | -0.0570477 | -0.0032584 | -0.0287158 | -0.0797702 | -0.0131473 | -0.0659226 | -0.0624234 | -0.0572546 | -0.0044494 | 0.0132861 | -0.0203047 | -0.0378500 | -0.0713006 | 0.0498557 | 0.0415523 | -0.0069743 | 0.0371596 | 0.0303815 | 0.0687216 | 1.0000000 | 0.1353085 | 0.0601750 | 0.0636661 | 0.1407342 | 0.0386211 | 0.0114365 | 0.1186788 | 0.0298580 | 0.0596798 | 0.0999683 | 0.7496827 | 0.0086745 |
| Score_PSI_04 | Score_PSI_04 | 0.0129772 | -0.0449077 | -0.0749480 | -0.0798121 | -0.0320167 | -0.0041080 | -0.0016542 | -0.0185639 | 0.0047573 | -0.0817158 | -0.1272277 | -0.0343542 | -0.0852573 | -0.0857042 | -0.0547617 | -0.0303324 | -0.0063422 | -0.0117228 | -0.0052418 | 0.0228481 | -0.0638071 | -0.1182074 | -0.0450913 | -0.0948401 | -0.1008569 | -0.0541503 | 0.0417007 | 0.0601831 | 0.0638412 | 0.0046272 | 0.1019307 | -0.0326986 | 0.0312780 | -0.0995591 | -0.0937443 | 0.0329404 | -0.0727333 | -0.0956269 | -0.0824732 | -0.0746093 | -0.0270607 | -0.0479955 | -0.1180160 | -0.1762469 | 0.0038509 | 0.2105379 | 0.1713379 | 0.2556384 | 0.2301195 | 0.2380935 | 0.1353085 | 1.0000000 | 0.0601419 | 0.0870693 | 0.1059485 | 0.0523892 | 0.0649032 | 0.0782559 | 0.0123489 | 0.0652098 | 0.1018205 | 0.1589978 | -0.0766302 |
| Score_PSI_06 | Score_PSI_06 | 0.0156305 | 0.0154891 | 0.0017920 | -0.0499866 | -0.0184090 | 0.0301520 | 0.0274588 | 0.0273930 | 0.0312923 | -0.0009316 | -0.0390292 | -0.0062045 | -0.0166476 | -0.0221487 | 0.0061083 | -0.0019546 | 0.0334780 | 0.0152153 | 0.0247052 | 0.0256121 | 0.0098029 | -0.0369162 | -0.0062861 | -0.0054742 | -0.0193730 | 0.0551291 | 0.0225916 | 0.0544576 | 0.0187794 | 0.0392571 | 0.0452860 | -0.0515403 | 0.0059688 | -0.0603629 | -0.0339321 | 0.0208887 | -0.0738364 | -0.0378140 | -0.0074196 | 0.0129386 | -0.0072465 | -0.0035153 | -0.0262166 | -0.0469516 | 0.0505415 | 0.0885083 | 0.0478268 | 0.0679149 | 0.0543554 | 0.0878847 | 0.0601750 | 0.0601419 | 1.0000000 | 0.0724291 | 0.1014588 | 0.0516246 | 0.0351464 | 0.1431056 | 0.0509831 | 0.0527115 | 0.0910520 | 0.1455340 | 0.0456525 |
| Score_PSI_08 | Score_PSI_08 | 0.0386634 | -0.0214412 | -0.0595834 | -0.1384081 | 0.0046992 | -0.0171646 | 0.0159553 | -0.0011887 | -0.0120380 | -0.0348072 | 0.0016051 | 0.0093687 | -0.0686735 | -0.0800058 | -0.0156145 | -0.0080622 | -0.0028885 | 0.0187552 | -0.0039459 | -0.0176853 | -0.0381375 | 0.0027541 | 0.0051695 | -0.0590998 | -0.0826240 | -0.0211509 | -0.0272232 | -0.0311579 | -0.0806516 | -0.0457654 | 0.0102293 | 0.0269822 | -0.0199006 | -0.0017558 | -0.0179990 | -0.0022962 | -0.0368684 | -0.0141213 | -0.0351047 | -0.0292526 | 0.0776413 | -0.0646189 | -0.0522876 | -0.1120007 | 0.0577776 | 0.1010348 | 0.0397571 | 0.1051698 | 0.0884315 | 0.1014879 | 0.0636661 | 0.0870693 | 0.0724291 | 1.0000000 | 0.0052449 | -0.0360093 | 0.0198090 | 0.0394605 | 0.0093444 | 0.0228045 | 0.0127268 | 0.0624052 | -0.0041983 |
| Score_PSI_09 | Score_PSI_09 | -0.0459321 | -0.0182303 | 0.0136214 | -0.0209899 | 0.0835767 | 0.0956403 | 0.0859168 | 0.0897240 | 0.0883277 | 0.0827573 | -0.0069280 | 0.0220979 | 0.0710883 | 0.0787681 | 0.0913137 | 0.0845264 | 0.0956884 | 0.0790559 | 0.0793646 | 0.0859133 | 0.0825062 | -0.0109302 | 0.0262747 | 0.0707774 | 0.0862428 | -0.0336614 | 0.0549990 | 0.0899361 | 0.0224833 | -0.0128057 | 0.0426036 | -0.0140788 | 0.0150699 | -0.0043396 | -0.0245784 | 0.0293689 | -0.0345921 | -0.0257851 | -0.0587674 | 0.0219699 | 0.0066697 | 0.0144757 | -0.0577213 | -0.0691325 | 0.0540124 | 0.0889343 | 0.0429090 | 0.0707269 | 0.0217880 | 0.0674377 | 0.1407342 | 0.1059485 | 0.1014588 | 0.0052449 | 1.0000000 | 0.0885278 | 0.0680540 | 0.1732337 | 0.0519119 | 0.1207438 | 0.2197254 | 0.2331017 | -0.0237660 |
| Score_PSI_10 | Score_PSI_10 | 0.0099239 | 0.0710046 | 0.0894074 | -0.0728092 | -0.0402520 | -0.0267891 | -0.0262995 | -0.0322016 | -0.0190535 | -0.0399586 | -0.0851994 | -0.0051357 | -0.0270735 | 0.0075443 | -0.0452172 | -0.0270956 | -0.0236698 | -0.0330533 | -0.0452983 | -0.0145875 | -0.0316508 | -0.0753999 | -0.0054371 | -0.0157615 | -0.0105068 | 0.0315830 | 0.0009222 | 0.0412713 | 0.0544528 | 0.0031479 | 0.0343100 | -0.0045339 | 0.0331569 | -0.0382369 | -0.0390884 | -0.0176156 | 0.0185638 | -0.0434036 | -0.0265019 | -0.0138235 | -0.0467887 | -0.0298356 | -0.0037803 | 0.0036343 | 0.0813038 | 0.1066619 | 0.0320669 | 0.0383771 | 0.0237048 | 0.0622532 | 0.0386211 | 0.0523892 | 0.0516246 | -0.0360093 | 0.0885278 | 1.0000000 | 0.1626632 | 0.1079488 | 0.2303938 | 0.0453739 | 0.0830134 | 0.2670390 | 0.0497447 |
| Score_PSI_11 | Score_PSI_11 | 0.1103031 | 0.1130121 | 0.0639191 | -0.0689026 | -0.1459187 | -0.1367919 | -0.1292050 | -0.1375039 | -0.1484276 | -0.1668264 | -0.1286917 | -0.0772394 | -0.1585705 | -0.1576927 | -0.1540619 | -0.1428877 | -0.1414366 | -0.1408040 | -0.1557561 | -0.1539039 | -0.1629600 | -0.1233582 | -0.0789391 | -0.1615833 | -0.1710237 | 0.1430203 | -0.0909811 | -0.0625676 | -0.0076797 | 0.0074122 | 0.0397075 | 0.0279871 | -0.0837910 | 0.0137283 | -0.0564170 | -0.0367514 | -0.0310002 | -0.0699820 | -0.0400763 | -0.0675289 | -0.0263715 | -0.0454564 | -0.0301775 | -0.0247178 | 0.1279724 | 0.1037006 | 0.0426574 | 0.0362529 | 0.0704445 | 0.0725381 | 0.0114365 | 0.0649032 | 0.0351464 | 0.0198090 | 0.0680540 | 0.1626632 | 1.0000000 | 0.1172504 | 0.2506376 | -0.0093577 | 0.0464067 | 0.5858033 | 0.1441986 |
| Score_PSI_12 | Score_PSI_12 | 0.0939499 | 0.1047402 | 0.0654692 | -0.0448292 | -0.0669148 | -0.0390690 | -0.0865925 | -0.0537461 | -0.0612852 | -0.0670446 | -0.0807406 | -0.0730231 | -0.0643414 | -0.0393015 | -0.0738343 | -0.0663743 | -0.0380979 | -0.0953948 | -0.0685223 | -0.0536226 | -0.0614646 | -0.0813748 | -0.0813685 | -0.0674588 | -0.0510987 | 0.1038617 | 0.1091949 | 0.0594933 | 0.1653812 | 0.0486693 | 0.0778917 | -0.0606628 | -0.0108546 | -0.0380145 | -0.0757690 | -0.0072258 | -0.0467304 | -0.0642500 | -0.0886155 | 0.0183344 | -0.0426109 | 0.0343065 | -0.0317534 | -0.0036828 | 0.1458258 | 0.0492328 | -0.0532586 | -0.0300702 | 0.0089560 | 0.0474896 | 0.1186788 | 0.0782559 | 0.1431056 | 0.0394605 | 0.1732337 | 0.1079488 | 0.1172504 | 1.0000000 | 0.1742084 | 0.0522204 | 0.1358951 | 0.3821290 | 0.0655557 |
| Score_PSI_13 | Score_PSI_13 | 0.1262199 | 0.1193336 | 0.0603447 | -0.0832889 | -0.1424419 | -0.1430591 | -0.1330731 | -0.1257130 | -0.1424055 | -0.1593474 | -0.1231640 | -0.1033157 | -0.1472927 | -0.1297623 | -0.1614933 | -0.1459851 | -0.1394736 | -0.1388091 | -0.1386420 | -0.1430776 | -0.1636005 | -0.1261494 | -0.1096072 | -0.1458284 | -0.1364494 | 0.0817412 | -0.0160408 | -0.0250714 | 0.0993570 | 0.0559662 | 0.0451515 | -0.0384416 | -0.0087678 | -0.0258097 | -0.0254596 | 0.0030403 | -0.0030434 | -0.0485933 | -0.0494623 | -0.0210595 | -0.0270503 | -0.0293121 | -0.0440578 | 0.0009109 | 0.1334619 | 0.0467554 | 0.0026944 | -0.0086832 | 0.0393676 | 0.0513975 | 0.0298580 | 0.0123489 | 0.0509831 | 0.0093444 | 0.0519119 | 0.2303938 | 0.2506376 | 0.1742084 | 1.0000000 | 0.0056987 | 0.0878105 | 0.4075564 | 0.0949467 |
| Score_PSI_14 | Score_PSI_14 | -0.0138581 | 0.0140012 | 0.0328153 | -0.0662610 | 0.0017770 | -0.0079719 | 0.0092188 | -0.0064032 | -0.0023314 | -0.0164980 | -0.0459005 | -0.0277664 | -0.0205437 | 0.0134380 | -0.0003415 | -0.0029199 | -0.0143285 | -0.0063199 | -0.0063967 | 0.0069610 | -0.0189605 | -0.0505286 | -0.0256155 | -0.0172231 | 0.0003983 | 0.0633107 | 0.0169512 | 0.0723872 | 0.0676972 | 0.0192234 | 0.0652307 | -0.0593032 | 0.0163779 | -0.0166146 | -0.0644595 | -0.0148982 | -0.0228048 | -0.0700502 | -0.0377753 | 0.0392025 | 0.0071556 | 0.0542351 | 0.0086141 | 0.0110118 | 0.0498603 | 0.0454462 | 0.0734846 | 0.0647245 | 0.0464407 | 0.0492194 | 0.0596798 | 0.0652098 | 0.0527115 | 0.0228045 | 0.1207438 | 0.0453739 | -0.0093577 | 0.0522204 | 0.0056987 | 1.0000000 | 0.1176726 | 0.0783006 | -0.0181150 |
| Score_PSI_15 | Score_PSI_15 | -0.0019014 | -0.0158282 | -0.0242059 | -0.0665941 | 0.0329127 | 0.0359419 | 0.0480143 | 0.0443475 | 0.0662158 | 0.0357574 | -0.0007875 | -0.0213211 | 0.0287970 | 0.0262619 | 0.0303266 | 0.0385911 | 0.0425664 | 0.0405341 | 0.0420468 | 0.0649921 | 0.0320446 | 0.0061946 | -0.0196124 | 0.0304634 | 0.0331751 | -0.0393782 | 0.0430812 | 0.0625753 | 0.0374530 | 0.0049833 | 0.0094012 | -0.0270588 | 0.0419068 | -0.0268805 | -0.0051105 | 0.0376698 | 0.0032891 | -0.0047427 | -0.0179532 | -0.0065909 | 0.0285160 | 0.0085622 | 0.0326658 | -0.0279567 | 0.0433809 | 0.0297688 | 0.0340007 | 0.0342374 | 0.0029691 | 0.0625191 | 0.0999683 | 0.1018205 | 0.0910520 | 0.0127268 | 0.2197254 | 0.0830134 | 0.0464067 | 0.1358951 | 0.0878105 | 0.1176726 | 1.0000000 | 0.2021298 | -0.0467071 |
| Score_PSI_90 | Score_PSI_90 | 0.0882354 | 0.0973882 | 0.0626347 | -0.0942117 | -0.1122411 | -0.0871039 | -0.1032756 | -0.1023631 | -0.0911960 | -0.1311288 | -0.1463724 | -0.0977954 | -0.1387904 | -0.1202753 | -0.1263561 | -0.1096342 | -0.0826332 | -0.1131291 | -0.1086247 | -0.0872152 | -0.1202132 | -0.1486299 | -0.1003157 | -0.1390570 | -0.1279752 | 0.1286484 | 0.0534106 | 0.0226639 | 0.0986077 | 0.0401196 | 0.0926476 | -0.0530033 | -0.0312757 | -0.0300979 | -0.1034135 | -0.0243705 | -0.0713756 | -0.0993958 | -0.0875069 | -0.0294786 | -0.0154089 | -0.0284310 | -0.0475558 | -0.0666219 | 0.1604802 | 0.1129695 | 0.0140214 | 0.0465081 | 0.0661595 | 0.1142992 | 0.7496827 | 0.1589978 | 0.1455340 | 0.0624052 | 0.2331017 | 0.2670390 | 0.5858033 | 0.3821290 | 0.4075564 | 0.0783006 | 0.2021298 | 1.0000000 | 0.1036455 |
| Payment_PAYM_90_HIP_KNEE | Payment_PAYM_90_HIP_KNEE | 0.2740999 | 0.2975679 | 0.1808580 | -0.1234255 | -0.2121841 | -0.1893123 | -0.1564699 | -0.1576660 | -0.2089917 | -0.1977109 | -0.0409475 | -0.0612456 | -0.2018848 | -0.2235957 | -0.1981220 | -0.2107792 | -0.1875503 | -0.1799323 | -0.1660056 | -0.2310210 | -0.2069970 | -0.0439537 | -0.0653730 | -0.2108956 | -0.2364653 | 0.1212071 | -0.0627505 | -0.0720431 | -0.0241965 | 0.0264108 | -0.0237710 | 0.0545394 | -0.0815209 | -0.0048449 | -0.0078309 | 0.0044851 | 0.0072647 | -0.0154257 | -0.0092648 | -0.1317922 | -0.0328044 | -0.1296356 | -0.1256363 | 0.0217206 | 0.3410864 | 0.0591548 | -0.0406696 | -0.0350247 | -0.0062985 | -0.0272101 | 0.0086745 | -0.0766302 | 0.0456525 | -0.0041983 | -0.0237660 | 0.0497447 | 0.1441986 | 0.0655557 | 0.0949467 | -0.0181150 | -0.0467071 | 0.1036455 | 1.0000000 |
Preprocessing
Variable Encoding (SE)
Identify Categorical Variables
# Create function to find categorical variables
is_categorical <- function(x) is.factor(x) | is.character(x)
# Apply function to all variables in the dataset
categorical_vars <- sapply(HipKneeClean, is_categorical)
# Print the names of all categorical variables
categorical <- names(HipKneeClean)[categorical_vars]
categorical## [1] "FacilityId" "EDV" "FacilityName" "State"Dummy encode the EDV column
# Define the encoding mapping (ignore NAs for now)
encoding_map <- c(
'low' = 1,
'medium' = 2,
'high' = 3,
'very high' = 4
)
# Dummy encoding used due to ordinal nature of this data
# Create a copy of HipKneeClean and name it HipKneeTrain to separate cleaned dataset and the training dataset
HipKneeTrain <- HipKneeClean %>%
mutate(EDV = recode(EDV, !!!encoding_map))
# Print first 20 rows of EDV column in HipKneeClean and HipKneeTrain to ensure proper encoding
cat("HipKneeClean")## HipKneeCleanprint(head(HipKneeClean$EDV, 20))## [1] "high" "high" "high" "low" "low" "high"
## [7] "low" "medium" "low" "medium" "low" "low"
## [13] "high" "high" "very high" "very high" "low" "high"
## [19] "low" "very high"cat("HipKneeTrain")## HipKneeTrainprint(head(HipKneeTrain$EDV, 20))## [1] 3 3 3 1 1 3 1 2 1 2 1 1 3 3 4 4 1 3 1 4Encode each state in alphabetical order
# Manually map out each state with their respective code in alphabetical order with a preceding 0 to make data non-ordinal
state_mapping <- c(
"AL" = "001",
"AK" = "002",
"AZ" = "003",
"AR" = "004",
"CA" = "005",
"CO" = "006",
"CT" = "007",
"DE" = "008",
"FL" = "009",
"GA" = "010",
"HI" = "011",
"ID" = "012",
"IL" = "013",
"IN" = "014",
"IA" = "015",
"KS" = "016",
"KY" = "017",
"LA" = "018",
"ME" = "019",
"MD" = "020",
"MA" = "021",
"MI" = "022",
"MN" = "023",
"MS" = "024",
"MO" = "025",
"MT" = "026",
"NE" = "027",
"NV" = "028",
"NH" = "029",
"NJ" = "030",
"NM" = "031",
"NY" = "032",
"NC" = "033",
"ND" = "034",
"OH" = "035",
"OK" = "036",
"OR" = "037",
"PA" = "038",
"RI" = "039",
"SC" = "040",
"SD" = "041",
"TN" = "042",
"TX" = "043",
"UT" = "044",
"VT" = "045",
"VA" = "046",
"WA" = "047",
"WV" = "048",
"WI" = "049",
"WY" = "050"
)
# Create new "StateCode" column with the encoded values
HipKneeTrain <- HipKneeTrain %>%
mutate(StateCode = state_mapping[State])
# Print 100 rows of the "State" and "StateCode" columns to ensure accuracy
print("State and StateCode Columns")## [1] "State and StateCode Columns"print(head(HipKneeTrain[c("State", "StateCode")], 100))## # A tibble: 100 × 2
## State StateCode
## <chr> <chr>
## 1 AL 001
## 2 AL 001
## 3 AL 001
## 4 AL 001
## 5 AL 001
## 6 AL 001
## 7 AL 001
## 8 AL 001
## 9 AL 001
## 10 AL 001
## # ℹ 90 more rows# Print all unique values in "StateCode" column to ensure accuracy
print("Unique StateCode Values")## [1] "Unique StateCode Values"print(unique(HipKneeTrain$StateCode))## [1] "001" "002" "003" "004" "005" "006" "007" "008" NA "009" "010" "011"
## [13] "012" "013" "014" "015" "016" "017" "018" "019" "020" "021" "022" "023"
## [25] "024" "025" "026" "027" "028" "029" "030" "031" "032" "033" "034" "035"
## [37] "036" "037" "038" "039" "040" "041" "042" "043" "044" "045" "046" "047"
## [49] "048" "049" "050"Collinearity and Feature Removal (SE)
Reassess collinearity with heatmap and correlation matrix
# Compute correlation matrix
cor_matrix <- cor(HipKneeTrain %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
# Convert correlation matrix to df
cor_table <- as.data.frame(cor_matrix)
# Add variable names as a column
cor_table$Variable <- rownames(cor_table)
# Reorder columns
cor_table <- cor_table %>%
select(Variable, everything())
# Print table
cor_table %>%
kable(caption = "Table 8. Correlation Coefficients Table") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | PredictedReadmissionRate_HIP_KNEE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | EDV | HCP_COVID_19 | IMM_3 | OP_18b | OP_29 | SAFE_USE_OF_OPIOIDS | VTE_1 | Score_COMP_HIP_KNEE | Score_MORT_30_AMI | Score_MORT_30_COPD | Score_MORT_30_HF | Score_MORT_30_PN | Score_MORT_30_STK | Score_PSI_03 | Score_PSI_04 | Score_PSI_06 | Score_PSI_08 | Score_PSI_09 | Score_PSI_10 | Score_PSI_11 | Score_PSI_12 | Score_PSI_13 | Score_PSI_14 | Score_PSI_15 | Payment_PAYM_90_HIP_KNEE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PredictedReadmissionRate_HIP_KNEE | PredictedReadmissionRate_HIP_KNEE | 1.0000000 | -0.2060912 | 0.1986939 | -0.0563082 | -0.0028840 | 0.1295727 | -0.0106510 | 0.1063002 | 0.0654668 | 0.3208550 | 0.0074065 | -0.0794948 | -0.1067828 | -0.0985660 | -0.0376746 | -0.0037334 | -0.0449077 | 0.0154891 | -0.0214412 | -0.0182303 | 0.0710046 | 0.1130121 | 0.1047402 | 0.1193336 | 0.0140012 | -0.0158282 | 0.2975679 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | -0.2060912 | 1.0000000 | -0.2262341 | 0.0302154 | 0.2182505 | -0.2448842 | 0.0865028 | 0.1100002 | -0.0248375 | -0.1091775 | -0.0952111 | -0.0230632 | 0.0300940 | -0.0702915 | -0.0760295 | -0.0499063 | -0.0948401 | -0.0054742 | -0.0590998 | 0.0707774 | -0.0157615 | -0.1615833 | -0.0674588 | -0.1458284 | -0.0172231 | 0.0304634 | -0.2108956 |
| EDV | EDV | 0.1986939 | -0.2262341 | 1.0000000 | 0.1599806 | 0.0038674 | 0.5918897 | 0.0603877 | -0.1223889 | 0.2992859 | -0.0240093 | -0.0687401 | -0.0840621 | -0.2588739 | -0.1904351 | -0.0754281 | 0.0292819 | -0.0438108 | -0.0308989 | -0.1637017 | -0.0181523 | 0.0837980 | 0.0455190 | 0.0749218 | 0.0724936 | 0.0399935 | 0.0144400 | 0.0053946 |
| HCP_COVID_19 | HCP_COVID_19 | -0.0563082 | 0.0302154 | 0.1599806 | 1.0000000 | 0.3203622 | 0.2574291 | 0.1067941 | -0.0812735 | 0.0241622 | -0.0510683 | -0.0869890 | -0.1128278 | -0.1245435 | -0.1523779 | -0.0988833 | 0.0943953 | 0.0417007 | 0.0225916 | -0.0272232 | 0.0549990 | 0.0009222 | -0.0909811 | 0.1091949 | -0.0160408 | 0.0169512 | 0.0430812 | -0.0627505 |
| IMM_3 | IMM_3 | -0.0028840 | 0.2182505 | 0.0038674 | 0.3203622 | 1.0000000 | 0.1105628 | 0.1317922 | 0.0410289 | 0.0906329 | -0.0212916 | -0.0165321 | -0.0616051 | -0.0010634 | -0.0761104 | 0.0146397 | 0.0451508 | 0.0601831 | 0.0544576 | -0.0311579 | 0.0899361 | 0.0412713 | -0.0625676 | 0.0594933 | -0.0250714 | 0.0723872 | 0.0625753 | -0.0720431 |
| OP_18b | OP_18b | 0.1295727 | -0.2448842 | 0.5918897 | 0.2574291 | 0.1105628 | 1.0000000 | 0.0506067 | -0.1400845 | 0.2344307 | -0.0293698 | -0.0678837 | -0.1527290 | -0.2195933 | -0.1858291 | -0.0905644 | 0.0583644 | 0.0638412 | 0.0187794 | -0.0806516 | 0.0224833 | 0.0544528 | -0.0076797 | 0.1653812 | 0.0993570 | 0.0676972 | 0.0374530 | -0.0241965 |
| OP_29 | OP_29 | -0.0106510 | 0.0865028 | 0.0603877 | 0.1067941 | 0.1317922 | 0.0506067 | 1.0000000 | -0.0650231 | 0.1567526 | -0.0096464 | -0.0600569 | 0.0081252 | 0.0099705 | -0.0536184 | -0.0251654 | -0.0032584 | 0.0312780 | 0.0059688 | -0.0199006 | 0.0150699 | 0.0331569 | -0.0837910 | -0.0108546 | -0.0087678 | 0.0163779 | 0.0419068 | -0.0815209 |
| SAFE_USE_OF_OPIOIDS | SAFE_USE_OF_OPIOIDS | 0.1063002 | 0.1100002 | -0.1223889 | -0.0812735 | 0.0410289 | -0.1400845 | -0.0650231 | 1.0000000 | -0.0563373 | -0.0081923 | -0.0643353 | -0.0362573 | 0.0171605 | -0.0204107 | -0.0804707 | -0.0287158 | -0.0995591 | -0.0603629 | -0.0017558 | -0.0043396 | -0.0382369 | 0.0137283 | -0.0380145 | -0.0258097 | -0.0166146 | -0.0268805 | -0.0048449 |
| VTE_1 | VTE_1 | 0.0654668 | -0.0248375 | 0.2992859 | 0.0241622 | 0.0906329 | 0.2344307 | 0.1567526 | -0.0563373 | 1.0000000 | -0.0526925 | -0.0493931 | -0.0282911 | -0.1051171 | -0.1710235 | -0.1238916 | -0.0378500 | -0.1180160 | -0.0262166 | -0.0522876 | -0.0577213 | -0.0037803 | -0.0301775 | -0.0317534 | -0.0440578 | 0.0086141 | 0.0326658 | -0.1256363 |
| Score_COMP_HIP_KNEE | Score_COMP_HIP_KNEE | 0.3208550 | -0.1091775 | -0.0240093 | -0.0510683 | -0.0212916 | -0.0293698 | -0.0096464 | -0.0081923 | -0.0526925 | 1.0000000 | 0.0830479 | -0.0203930 | -0.0007242 | 0.0241066 | 0.0211621 | 0.0498557 | 0.0038509 | 0.0505415 | 0.0577776 | 0.0540124 | 0.0813038 | 0.1279724 | 0.1458258 | 0.1334619 | 0.0498603 | 0.0433809 | 0.3410864 |
| Score_MORT_30_AMI | Score_MORT_30_AMI | 0.0074065 | -0.0952111 | -0.0687401 | -0.0869890 | -0.0165321 | -0.0678837 | -0.0600569 | -0.0643353 | -0.0493931 | 0.0830479 | 1.0000000 | 0.2498600 | 0.3407616 | 0.3309425 | 0.2222539 | 0.0415523 | 0.2105379 | 0.0885083 | 0.1010348 | 0.0889343 | 0.1066619 | 0.1037006 | 0.0492328 | 0.0467554 | 0.0454462 | 0.0297688 | 0.0591548 |
| Score_MORT_30_COPD | Score_MORT_30_COPD | -0.0794948 | -0.0230632 | -0.0840621 | -0.1128278 | -0.0616051 | -0.1527290 | 0.0081252 | -0.0362573 | -0.0282911 | -0.0203930 | 0.2498600 | 1.0000000 | 0.3844105 | 0.3710744 | 0.2038243 | -0.0069743 | 0.1713379 | 0.0478268 | 0.0397571 | 0.0429090 | 0.0320669 | 0.0426574 | -0.0532586 | 0.0026944 | 0.0734846 | 0.0340007 | -0.0406696 |
| Score_MORT_30_HF | Score_MORT_30_HF | -0.1067828 | 0.0300940 | -0.2588739 | -0.1245435 | -0.0010634 | -0.2195933 | 0.0099705 | 0.0171605 | -0.1051171 | -0.0007242 | 0.3407616 | 0.3844105 | 1.0000000 | 0.4479367 | 0.3147371 | 0.0371596 | 0.2556384 | 0.0679149 | 0.1051698 | 0.0707269 | 0.0383771 | 0.0362529 | -0.0300702 | -0.0086832 | 0.0647245 | 0.0342374 | -0.0350247 |
| Score_MORT_30_PN | Score_MORT_30_PN | -0.0985660 | -0.0702915 | -0.1904351 | -0.1523779 | -0.0761104 | -0.1858291 | -0.0536184 | -0.0204107 | -0.1710235 | 0.0241066 | 0.3309425 | 0.3710744 | 0.4479367 | 1.0000000 | 0.3042563 | 0.0303815 | 0.2301195 | 0.0543554 | 0.0884315 | 0.0217880 | 0.0237048 | 0.0704445 | 0.0089560 | 0.0393676 | 0.0464407 | 0.0029691 | -0.0062985 |
| Score_MORT_30_STK | Score_MORT_30_STK | -0.0376746 | -0.0760295 | -0.0754281 | -0.0988833 | 0.0146397 | -0.0905644 | -0.0251654 | -0.0804707 | -0.1238916 | 0.0211621 | 0.2222539 | 0.2038243 | 0.3147371 | 0.3042563 | 1.0000000 | 0.0687216 | 0.2380935 | 0.0878847 | 0.1014879 | 0.0674377 | 0.0622532 | 0.0725381 | 0.0474896 | 0.0513975 | 0.0492194 | 0.0625191 | -0.0272101 |
| Score_PSI_03 | Score_PSI_03 | -0.0037334 | -0.0499063 | 0.0292819 | 0.0943953 | 0.0451508 | 0.0583644 | -0.0032584 | -0.0287158 | -0.0378500 | 0.0498557 | 0.0415523 | -0.0069743 | 0.0371596 | 0.0303815 | 0.0687216 | 1.0000000 | 0.1353085 | 0.0601750 | 0.0636661 | 0.1407342 | 0.0386211 | 0.0114365 | 0.1186788 | 0.0298580 | 0.0596798 | 0.0999683 | 0.0086745 |
| Score_PSI_04 | Score_PSI_04 | -0.0449077 | -0.0948401 | -0.0438108 | 0.0417007 | 0.0601831 | 0.0638412 | 0.0312780 | -0.0995591 | -0.1180160 | 0.0038509 | 0.2105379 | 0.1713379 | 0.2556384 | 0.2301195 | 0.2380935 | 0.1353085 | 1.0000000 | 0.0601419 | 0.0870693 | 0.1059485 | 0.0523892 | 0.0649032 | 0.0782559 | 0.0123489 | 0.0652098 | 0.1018205 | -0.0766302 |
| Score_PSI_06 | Score_PSI_06 | 0.0154891 | -0.0054742 | -0.0308989 | 0.0225916 | 0.0544576 | 0.0187794 | 0.0059688 | -0.0603629 | -0.0262166 | 0.0505415 | 0.0885083 | 0.0478268 | 0.0679149 | 0.0543554 | 0.0878847 | 0.0601750 | 0.0601419 | 1.0000000 | 0.0724291 | 0.1014588 | 0.0516246 | 0.0351464 | 0.1431056 | 0.0509831 | 0.0527115 | 0.0910520 | 0.0456525 |
| Score_PSI_08 | Score_PSI_08 | -0.0214412 | -0.0590998 | -0.1637017 | -0.0272232 | -0.0311579 | -0.0806516 | -0.0199006 | -0.0017558 | -0.0522876 | 0.0577776 | 0.1010348 | 0.0397571 | 0.1051698 | 0.0884315 | 0.1014879 | 0.0636661 | 0.0870693 | 0.0724291 | 1.0000000 | 0.0052449 | -0.0360093 | 0.0198090 | 0.0394605 | 0.0093444 | 0.0228045 | 0.0127268 | -0.0041983 |
| Score_PSI_09 | Score_PSI_09 | -0.0182303 | 0.0707774 | -0.0181523 | 0.0549990 | 0.0899361 | 0.0224833 | 0.0150699 | -0.0043396 | -0.0577213 | 0.0540124 | 0.0889343 | 0.0429090 | 0.0707269 | 0.0217880 | 0.0674377 | 0.1407342 | 0.1059485 | 0.1014588 | 0.0052449 | 1.0000000 | 0.0885278 | 0.0680540 | 0.1732337 | 0.0519119 | 0.1207438 | 0.2197254 | -0.0237660 |
| Score_PSI_10 | Score_PSI_10 | 0.0710046 | -0.0157615 | 0.0837980 | 0.0009222 | 0.0412713 | 0.0544528 | 0.0331569 | -0.0382369 | -0.0037803 | 0.0813038 | 0.1066619 | 0.0320669 | 0.0383771 | 0.0237048 | 0.0622532 | 0.0386211 | 0.0523892 | 0.0516246 | -0.0360093 | 0.0885278 | 1.0000000 | 0.1626632 | 0.1079488 | 0.2303938 | 0.0453739 | 0.0830134 | 0.0497447 |
| Score_PSI_11 | Score_PSI_11 | 0.1130121 | -0.1615833 | 0.0455190 | -0.0909811 | -0.0625676 | -0.0076797 | -0.0837910 | 0.0137283 | -0.0301775 | 0.1279724 | 0.1037006 | 0.0426574 | 0.0362529 | 0.0704445 | 0.0725381 | 0.0114365 | 0.0649032 | 0.0351464 | 0.0198090 | 0.0680540 | 0.1626632 | 1.0000000 | 0.1172504 | 0.2506376 | -0.0093577 | 0.0464067 | 0.1441986 |
| Score_PSI_12 | Score_PSI_12 | 0.1047402 | -0.0674588 | 0.0749218 | 0.1091949 | 0.0594933 | 0.1653812 | -0.0108546 | -0.0380145 | -0.0317534 | 0.1458258 | 0.0492328 | -0.0532586 | -0.0300702 | 0.0089560 | 0.0474896 | 0.1186788 | 0.0782559 | 0.1431056 | 0.0394605 | 0.1732337 | 0.1079488 | 0.1172504 | 1.0000000 | 0.1742084 | 0.0522204 | 0.1358951 | 0.0655557 |
| Score_PSI_13 | Score_PSI_13 | 0.1193336 | -0.1458284 | 0.0724936 | -0.0160408 | -0.0250714 | 0.0993570 | -0.0087678 | -0.0258097 | -0.0440578 | 0.1334619 | 0.0467554 | 0.0026944 | -0.0086832 | 0.0393676 | 0.0513975 | 0.0298580 | 0.0123489 | 0.0509831 | 0.0093444 | 0.0519119 | 0.2303938 | 0.2506376 | 0.1742084 | 1.0000000 | 0.0056987 | 0.0878105 | 0.0949467 |
| Score_PSI_14 | Score_PSI_14 | 0.0140012 | -0.0172231 | 0.0399935 | 0.0169512 | 0.0723872 | 0.0676972 | 0.0163779 | -0.0166146 | 0.0086141 | 0.0498603 | 0.0454462 | 0.0734846 | 0.0647245 | 0.0464407 | 0.0492194 | 0.0596798 | 0.0652098 | 0.0527115 | 0.0228045 | 0.1207438 | 0.0453739 | -0.0093577 | 0.0522204 | 0.0056987 | 1.0000000 | 0.1176726 | -0.0181150 |
| Score_PSI_15 | Score_PSI_15 | -0.0158282 | 0.0304634 | 0.0144400 | 0.0430812 | 0.0625753 | 0.0374530 | 0.0419068 | -0.0268805 | 0.0326658 | 0.0433809 | 0.0297688 | 0.0340007 | 0.0342374 | 0.0029691 | 0.0625191 | 0.0999683 | 0.1018205 | 0.0910520 | 0.0127268 | 0.2197254 | 0.0830134 | 0.0464067 | 0.1358951 | 0.0878105 | 0.1176726 | 1.0000000 | -0.0467071 |
| Payment_PAYM_90_HIP_KNEE | Payment_PAYM_90_HIP_KNEE | 0.2975679 | -0.2108956 | 0.0053946 | -0.0627505 | -0.0720431 | -0.0241965 | -0.0815209 | -0.0048449 | -0.1256363 | 0.3410864 | 0.0591548 | -0.0406696 | -0.0350247 | -0.0062985 | -0.0272101 | 0.0086745 | -0.0766302 | 0.0456525 | -0.0041983 | -0.0237660 | 0.0497447 | 0.1441986 | 0.0655557 | 0.0949467 | -0.0181150 | -0.0467071 | 1.0000000 |
Imputation and Handling of Missing Values (AC)
# Remove all NA values in target variable "PredictedReadmissionRate_HIP_KNEE"
HipKneeTrain <- HipKneeTrain %>% filter(!is.na(PredictedReadmissionRate_HIP_KNEE))
# Remove all NA values in the "State", "StateCode", and "FacilityName" columns
HipKneeTrain <- HipKneeTrain %>% drop_na(State, StateCode, FacilityName)
# Print number of remaining variables and observations
dimensions <- dim(HipKneeTrain)
cat("Number of variables:", dimensions[2], "\n")## Number of variables: 31cat("Number of observations:", dimensions[1], "\n")## Number of observations: 1833We decided to remove the one facility that had an NA value, which also happened to be the same observation with a missing state value.
# Calculate missing values
missing_values_summary <- HipKneeTrain %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeTrain)) * 100)
# Print table
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0.0000000 |
| PredictedReadmissionRate_HIP_KNEE | 0 | 0.0000000 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 33 | 1.8003273 |
| EDV | 90 | 4.9099836 |
| HCP_COVID_19 | 16 | 0.8728860 |
| IMM_3 | 16 | 0.8728860 |
| OP_18b | 75 | 4.0916530 |
| OP_29 | 222 | 12.1112930 |
| SAFE_USE_OF_OPIOIDS | 69 | 3.7643208 |
| VTE_1 | 994 | 54.2280415 |
| Score_COMP_HIP_KNEE | 40 | 2.1822149 |
| Score_MORT_30_AMI | 405 | 22.0949264 |
| Score_MORT_30_COPD | 247 | 13.4751773 |
| Score_MORT_30_HF | 141 | 7.6923077 |
| Score_MORT_30_PN | 125 | 6.8194217 |
| Score_MORT_30_STK | 284 | 15.4937261 |
| Score_PSI_03 | 8 | 0.4364430 |
| Score_PSI_04 | 575 | 31.3693399 |
| Score_PSI_06 | 2 | 0.1091107 |
| Score_PSI_08 | 2 | 0.1091107 |
| Score_PSI_09 | 2 | 0.1091107 |
| Score_PSI_10 | 41 | 2.2367703 |
| Score_PSI_11 | 40 | 2.1822149 |
| Score_PSI_12 | 2 | 0.1091107 |
| Score_PSI_13 | 42 | 2.2913257 |
| Score_PSI_14 | 87 | 4.7463175 |
| Score_PSI_15 | 29 | 1.5821058 |
| FacilityName | 0 | 0.0000000 |
| State | 0 | 0.0000000 |
| Payment_PAYM_90_HIP_KNEE | 42 | 2.2913257 |
| StateCode | 0 | 0.0000000 |
Impute variables with low percentage missingness (<5%) by the median for numeric variables
# Calculate median for columns with <5% missing values
numeric_vars_low_missing <- c("HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE", "EDV", "HCP_COVID_19", "IMM_3", "OP_18b", "SAFE_USE_OF_OPIOIDS", "Score_COMP_HIP_KNEE", "Score_PSI_03", "Score_PSI_06", "Score_PSI_08", "Score_PSI_09", "Score_PSI_10", "Score_PSI_11", "Score_PSI_12", "Score_PSI_13", "Score_PSI_14", "Score_PSI_15", "Payment_PAYM_90_HIP_KNEE")
for (var in numeric_vars_low_missing) {
HipKneeTrain[[var]][is.na(HipKneeTrain[[var]])] <- median(HipKneeTrain[[var]], na.rm = TRUE)
}Impute high percentage missingness variables (>5%) using KNN
# Select high missingness variables for KNN imputation
vars_for_knn <- c("VTE_1", "Score_MORT_30_AMI", "Score_MORT_30_COPD", "Score_MORT_30_HF", "Score_MORT_30_PN", "Score_MORT_30_STK", "Score_PSI_04", "OP_29")
# Perform KNN imputation
HipKneeTrain_knn <- kNN(HipKneeTrain, variable = vars_for_knn, k = 5)
# Remove columns created by the KNN function
HipKneeTrain_knn <- HipKneeTrain_knn %>% select(-ends_with("_imp"))
# Update HipKneeTrain with imputed values
HipKneeTrain[vars_for_knn] <- HipKneeTrain_knn[vars_for_knn]Is this a good method for imputing missing values? We decided that many of our variables had very low missingness percentage, <1%. So, Median imputation would be fine in this case. For the few variables that had higher missingness we went with KNN imputation. Do you have any suggestions or ideas that would be more appropriate here?
# Calculate missing values
missing_values_summary <- HipKneeTrain %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeTrain)) * 100)
# Print table
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0 |
| PredictedReadmissionRate_HIP_KNEE | 0 | 0 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 0 | 0 |
| EDV | 0 | 0 |
| HCP_COVID_19 | 0 | 0 |
| IMM_3 | 0 | 0 |
| OP_18b | 0 | 0 |
| OP_29 | 0 | 0 |
| SAFE_USE_OF_OPIOIDS | 0 | 0 |
| VTE_1 | 0 | 0 |
| Score_COMP_HIP_KNEE | 0 | 0 |
| Score_MORT_30_AMI | 0 | 0 |
| Score_MORT_30_COPD | 0 | 0 |
| Score_MORT_30_HF | 0 | 0 |
| Score_MORT_30_PN | 0 | 0 |
| Score_MORT_30_STK | 0 | 0 |
| Score_PSI_03 | 0 | 0 |
| Score_PSI_04 | 0 | 0 |
| Score_PSI_06 | 0 | 0 |
| Score_PSI_08 | 0 | 0 |
| Score_PSI_09 | 0 | 0 |
| Score_PSI_10 | 0 | 0 |
| Score_PSI_11 | 0 | 0 |
| Score_PSI_12 | 0 | 0 |
| Score_PSI_13 | 0 | 0 |
| Score_PSI_14 | 0 | 0 |
| Score_PSI_15 | 0 | 0 |
| FacilityName | 0 | 0 |
| State | 0 | 0 |
| Payment_PAYM_90_HIP_KNEE | 0 | 0 |
| StateCode | 0 | 0 |
Feature Engineer Mortality Data (AC)
# Average death rates amongst mortality variables and create new column "Score_Ovr_MORT"
HipKneeTrain$Score_Ovr_MORT <- rowMeans(HipKneeTrain[, c("Score_MORT_30_AMI",
"Score_MORT_30_COPD",
"Score_MORT_30_HF",
"Score_MORT_30_PN",
"Score_MORT_30_STK")],
na.rm = TRUE)
# Remove old mortality columns
HipKneeTrain <- HipKneeTrain[, !(names(HipKneeTrain) %in% c("Score_MORT_30_AMI",
"Score_MORT_30_COPD",
"Score_MORT_30_HF",
"Score_MORT_30_PN",
"Score_MORT_30_STK"))]Scaling and Normalizing
# Exclude non-numeric columns
non_numeric_columns <- c("State", "FacilityName", "FacilityId", "StateCode")
numeric_columns <- setdiff(names(HipKneeTrain), non_numeric_columns)
# Scale and normalize the numeric columns
HipKneeTrain_scaled <- as.data.frame(scale(HipKneeTrain[, numeric_columns]))
# Combine the scaled data with the non-numeric data
HipKneeTrain <- cbind(HipKneeTrain_scaled, HipKneeTrain[, non_numeric_columns])
# Print the first few rows of the scaled and normalized dataset
head(HipKneeTrain)## PredictedReadmissionRate_HIP_KNEE
## 1 -1.1150985
## 2 -0.8611924
## 3 1.0674602
## 4 -0.2314305
## 5 -0.6125646
## 6 -1.3887980
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE EDV HCP_COVID_19
## 1 0.6111897 0.2980455 -0.85464780
## 2 -0.4631912 0.2980455 -0.94878379
## 3 -1.2689769 0.2980455 -1.03246023
## 4 0.6111897 -1.6530882 -3.23942637
## 5 0.3425945 0.2980455 -0.09110027
## 6 -1.0003817 -1.6530882 -1.99473930
## IMM_3 OP_18b OP_29 SAFE_USE_OF_OPIOIDS VTE_1
## 1 0.88316647 0.5118139 -3.8452578 -0.3699263 0.756471728
## 2 0.03799359 -0.8569912 0.3270810 0.8064701 -0.008043768
## 3 -0.69448958 -0.2531066 -0.6095664 0.3359116 -0.008043768
## 4 -1.48331760 -1.1991925 -5.8888523 -0.1346470 -0.335693267
## 5 0.09433845 0.3306485 0.6676801 1.0417494 -1.318641762
## 6 -0.80717930 -0.5953079 0.6676801 -0.3699263 -0.226476767
## Score_COMP_HIP_KNEE Score_PSI_03 Score_PSI_04 Score_PSI_06 Score_PSI_08
## 1 -0.8663258 -0.3816018 0.833989052 -0.3705681 1.226111221
## 2 -1.5883789 0.6794088 0.771436453 -0.5915348 0.006652801
## 3 2.5634263 1.5475084 0.253143493 2.5019979 -1.212805619
## 4 -0.1442727 -0.3237285 -0.001272118 -0.1496015 0.006652801
## 5 0.5777803 -0.8831704 1.075789013 -0.1496015 -1.212805619
## 6 1.1193201 -0.3430196 0.771436453 -0.1496015 0.006652801
## Score_PSI_09 Score_PSI_10 Score_PSI_11 Score_PSI_12 Score_PSI_13 Score_PSI_14
## 1 -0.25064332 -1.16016845 1.5615798 1.1419636 2.67912966 -0.8980388
## 2 0.35503765 -0.54384358 -0.5718768 -0.7209359 -0.80202648 -0.3937431
## 3 5.88692387 -0.83860765 -0.5140421 -0.2803853 -0.88929781 -0.7019238
## 4 -0.04874966 -0.00790891 -0.1670341 0.3867341 -0.09415908 -0.2256446
## 5 -1.28030097 0.39404209 -1.5807704 -0.6831744 0.25492622 -0.4217595
## 6 -0.14969649 -0.16868931 -0.3373251 -0.3307339 -0.39476030 1.2592260
## Score_PSI_15 Payment_PAYM_90_HIP_KNEE Score_Ovr_MORT State
## 1 -0.52773226 0.59439362 -0.4803681 AL
## 2 -0.58885917 -1.55714742 2.0163981 AL
## 3 0.72536925 0.43284798 1.7753310 AL
## 4 -0.06928049 -0.07442593 3.4283624 AL
## 5 0.23635402 0.48635354 0.8455008 AL
## 6 -0.19153430 1.28636461 1.4826067 AL
## FacilityName FacilityId StateCode
## 1 SOUTHEAST HEALTH MEDICAL CENTER 010001 001
## 2 MARSHALL MEDICAL CENTERS 010005 001
## 3 NORTH ALABAMA MEDICAL CENTER 010006 001
## 4 MIZELL MEMORIAL HOSPITAL 010007 001
## 5 ST. VINCENT'S EAST 010011 001
## 6 DEKALB REGIONAL MEDICAL CENTER 010012 001Reassess heatmap with engineered mortality data
# Compute correlation matrix
cor_matrix <- cor(HipKneeTrain %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
Saving the dataset for future ease of use
save(HipKneeTrain, file = "HipKneeTrain.RData")Preprocessing the test dataset (AC)
We are utilizing the most recent snapshot from 04/24/2024 as our test set. Utilizing this brand new data will help to ensure that our model is generalizable and useful for future analyses.
Loading the Data
Note: Data must be saved to personal computer and the filepath must be specified for each user due to the size of the files. The data can be accessed here. Download the snapshot from 4/24/2024.
# Set the directory for the data files - must be saved to personal computer (too large for GitHub). Can be accessed here: https://data.cms.gov/provider-data/archived-data/hospitals
filepath <- "/Users/seugley/Desktop/hospitals_04_2024/"
# List the files in the directory that have "Hospital.csv"
files <- list.files(path = filepath, pattern = "Hospital.csv")
# Iterate through each file in the list
for(f in 1:length(files)) {
# Read the CSV, clean column names to upper camel case, and store in "dat"
dat <- clean_names(read_csv(paste0(filepath, files[f]),
show_col_types = FALSE),
case = "upper_camel")
# Remove ".Hospital.csv" part of the file names to create variable name
filename <- gsub(".Hospital\\.csv", "", files[f])
# Assign data to a variable with the above created name
assign(filename, dat)
}
# Create a df of file names without ".Hospital.csv"
files <- gsub(".Hospital\\.csv", "", files) %>% data.frame()
# Set column name of the df to "File Name"
names(files) <- "File Name"
files %>%
kable(
format = "html",
caption = "Table 1. List of hospital-level data files.") %>%
kable_styling(bootstrap_options = c("striped", full_width = F)
)| File Name |
|---|
| Complications_and_Deaths |
| FY_2024_HAC_Reduction_Program |
| FY_2024_Hospital_Readmissions_Reduction_Program |
| HCAHPS |
| Healthcare_Associated_Infections |
| Maternal_Health |
| Medicare_Hospital_Spending_Per_Patient |
| Outpatient_Imaging_Efficiency |
| Payment_and_Value_of_Care |
| Timely_and_Effective_Care |
| Unplanned_Hospital_Visits |
Exploring and Preprocessing the FY_2024_Hospital_Readmissions_Reduction_Program dataset (AC)
Viewing and checking for missing values
# Display first 10 rows of FY_2024_Hospital_Readmissions_Reduction_Program
head(FY_2024_Hospital_Readmissions_Reduction_Program,10)## # A tibble: 10 × 12
## FacilityName FacilityId State MeasureName NumberOfDischarges Footnote
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH ME… 010001 AL READM-30-H… N/A NA
## 2 SOUTHEAST HEALTH ME… 010001 AL READM-30-H… 616 NA
## 3 SOUTHEAST HEALTH ME… 010001 AL READM-30-A… 274 NA
## 4 SOUTHEAST HEALTH ME… 010001 AL READM-30-P… 404 NA
## 5 SOUTHEAST HEALTH ME… 010001 AL READM-30-C… 126 NA
## 6 SOUTHEAST HEALTH ME… 010001 AL READM-30-C… 117 NA
## 7 MARSHALL MEDICAL CE… 010005 AL READM-30-A… N/A 1
## 8 MARSHALL MEDICAL CE… 010005 AL READM-30-C… 137 NA
## 9 MARSHALL MEDICAL CE… 010005 AL READM-30-P… 285 NA
## 10 MARSHALL MEDICAL CE… 010005 AL READM-30-H… 129 NA
## # ℹ 6 more variables: ExcessReadmissionRatio <chr>,
## # PredictedReadmissionRate <chr>, ExpectedReadmissionRate <chr>,
## # NumberOfReadmissions <chr>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## Footnote
## 12077Replacing values with NA and “Too Few to Report” values with “5”
# Use the function "replace_with_na_all()" to replace aberrant values with NA
FY_2024_Hospital_Readmissions_Reduction_Program <- replace_with_na_all(FY_2024_Hospital_Readmissions_Reduction_Program, condition = ~ .x == "N/A")
# Replace "Too Few to Report" values with "5" in using gsub
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions <- gsub("Too Few to Report", "5", FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions)
# Check first 10 rows to confirm that it worked
head(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions, 10)## [1] "5" "149" "32" "68" "11" "20" NA "14" "40" "24"# NumberOfReadmissions had to be converted to numeric before applying integers
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions <- as.numeric(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions)
# Find all values of "5" in NumberOfReadmissions
fives <- which(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions == 5)
# Replace values of "5" with random integers from 1 - 10
FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions[fives] <- sample(1:10, length(fives), replace = TRUE)
# Check the first 20 rows to see if this was applied correctly
head(FY_2024_Hospital_Readmissions_Reduction_Program$NumberOfReadmissions,20)## [1] 3 149 32 68 11 20 NA 14 40 24 1 NA 7 21 15 83 36 75 5
## [20] NAConverting columns to numeric
# Selecting the columns to convert
columns_to_convert <- c("NumberOfDischarges", "ExcessReadmissionRatio", "PredictedReadmissionRate", "ExpectedReadmissionRate", "NumberOfReadmissions")
# Use mutate_at to convert the specified columns to numeric
FY_2024_Hospital_Readmissions_Reduction_Program <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
mutate_at(vars(one_of(columns_to_convert)), as.numeric)
# Print the structure of the dataframe to check the changes
str(FY_2024_Hospital_Readmissions_Reduction_Program)## tibble [18,774 × 12] (S3: tbl_df/tbl/data.frame)
## $ FacilityName : chr [1:18774] "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" "SOUTHEAST HEALTH MEDICAL CENTER" ...
## $ FacilityId : chr [1:18774] "010001" "010001" "010001" "010001" ...
## $ State : chr [1:18774] "AL" "AL" "AL" "AL" ...
## $ MeasureName : chr [1:18774] "READM-30-HIP-KNEE-HRRP" "READM-30-HF-HRRP" "READM-30-AMI-HRRP" "READM-30-PN-HRRP" ...
## $ NumberOfDischarges : num [1:18774] NA 616 274 404 126 117 NA 137 285 129 ...
## $ Footnote : num [1:18774] NA NA NA NA NA NA 1 NA NA NA ...
## $ ExcessReadmissionRatio : num [1:18774] 0.892 1.1 0.933 0.987 0.952 ...
## $ PredictedReadmissionRate: num [1:18774] 3.53 23.13 12.9 17.05 9.81 ...
## $ ExpectedReadmissionRate : num [1:18774] 3.96 21.02 13.83 17.28 10.31 ...
## $ NumberOfReadmissions : num [1:18774] 3 149 32 68 11 20 NA 14 40 24 ...
## $ StartDate : chr [1:18774] "07/01/2019" "07/01/2019" "07/01/2019" "07/01/2019" ...
## $ EndDate : chr [1:18774] "06/30/2022" "06/30/2022" "06/30/2022" "06/30/2022" ...Removing excess text from measure names
FY_2024_Hospital_Readmissions_Reduction_Program <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
mutate(MeasureName = gsub("READM-30-", "", MeasureName)) %>%
mutate(MeasureName = gsub("-HRRP", "", MeasureName)) Pivoting the data wider
readmissionsClean <- FY_2024_Hospital_Readmissions_Reduction_Program %>%
pivot_wider(
names_from = MeasureName,
values_from = c(NumberOfDischarges, ExcessReadmissionRatio, PredictedReadmissionRate, ExpectedReadmissionRate, NumberOfReadmissions),
id_cols = c(FacilityName, FacilityId, State, StartDate, EndDate)
)
# Check the new dataframe
dim(readmissionsClean)## [1] 3129 35head(readmissionsClean)## # A tibble: 6 × 35
## FacilityName FacilityId State StartDate EndDate NumberOfDischarges_H…¹
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH ME… 010001 AL 07/01/20… 06/30/… NA
## 2 MARSHALL MEDICAL CE… 010005 AL 07/01/20… 06/30/… NA
## 3 NORTH ALABAMA MEDIC… 010006 AL 07/01/20… 06/30/… NA
## 4 MIZELL MEMORIAL HOS… 010007 AL 07/01/20… 06/30/… NA
## 5 CRENSHAW COMMUNITY … 010008 AL 07/01/20… 06/30/… NA
## 6 ST. VINCENT'S EAST 010011 AL 07/01/20… 06/30/… NA
## # ℹ abbreviated name: ¹`NumberOfDischarges_HIP-KNEE`
## # ℹ 29 more variables: NumberOfDischarges_HF <dbl>,
## # NumberOfDischarges_AMI <dbl>, NumberOfDischarges_PN <dbl>,
## # NumberOfDischarges_CABG <dbl>, NumberOfDischarges_COPD <dbl>,
## # `ExcessReadmissionRatio_HIP-KNEE` <dbl>, ExcessReadmissionRatio_HF <dbl>,
## # ExcessReadmissionRatio_AMI <dbl>, ExcessReadmissionRatio_PN <dbl>,
## # ExcessReadmissionRatio_CABG <dbl>, ExcessReadmissionRatio_COPD <dbl>, …Filtering for only hip/knee conditions
readmissionsClean <- readmissionsClean %>%
select(FacilityName, FacilityId, State, matches("HIP-KNEE$"))Exploring and Preprocessing the HCAHPS dataset
Viewing and checking for missing values
# Display first 10 rows of HCAHPS
head(HCAHPS,10)## # A tibble: 10 × 22
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 15 more variables: TelephoneNumber <chr>, HcahpsMeasureId <chr>,
## # HcahpsQuestion <chr>, HcahpsAnswerDescription <chr>,
## # PatientSurveyStarRating <chr>, PatientSurveyStarRatingFootnote <dbl>,
## # HcahpsAnswerPercent <chr>, HcahpsAnswerPercentFootnote <chr>,
## # HcahpsLinearMeanValue <chr>, NumberOfCompletedSurveys <chr>,
## # NumberOfCompletedSurveysFootnote <chr>, SurveyResponseRatePercent <chr>,
## # SurveyResponseRatePercentFootnote <chr>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- HCAHPS %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## PatientSurveyStarRatingFootnote
## 430641Removing footnote columns and replacing NA values
# Removing all footnote columns
HCAHPS <- HCAHPS %>%
select(-ends_with("footnote"))
# Replacing all "Not Applicable" with NA
HCAHPS <- as.data.frame(sapply(HCAHPS, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
HCAHPS <- as.data.frame(sapply(HCAHPS, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Pivoting the data wider
HCAHPSClean <- HCAHPS %>%
pivot_wider(
names_from = HcahpsMeasureId,
values_from = c(PatientSurveyStarRating, HcahpsAnswerPercent, HcahpsLinearMeanValue, SurveyResponseRatePercent),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(HCAHPSClean)## [1] 4814 375head(HCAHPSClean)## # A tibble: 6 × 375
## FacilityName FacilityId State PatientSurveyStarRat…¹ PatientSurveyStarRat…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL <NA> <NA>
## 2 MARSHALL MEDIC… 010005 AL <NA> <NA>
## 3 NORTH ALABAMA … 010006 AL <NA> <NA>
## 4 MIZELL MEMORIA… 010007 AL <NA> <NA>
## 5 CRENSHAW COMMU… 010008 AL <NA> <NA>
## 6 ST. VINCENT'S … 010011 AL <NA> <NA>
## # ℹ abbreviated names: ¹PatientSurveyStarRating_H_COMP_1_A_P,
## # ²PatientSurveyStarRating_H_COMP_1_SN_P
## # ℹ 370 more variables: PatientSurveyStarRating_H_COMP_1_U_P <chr>,
## # PatientSurveyStarRating_H_COMP_1_LINEAR_SCORE <chr>,
## # PatientSurveyStarRating_H_COMP_1_STAR_RATING <chr>,
## # PatientSurveyStarRating_H_NURSE_RESPECT_A_P <chr>,
## # PatientSurveyStarRating_H_NURSE_RESPECT_SN_P <chr>, …Exploring and Preprocessing the Timely_and_Effective_Care dataset
Viewing and checking for missing values
# Display first 10 rows of Timely_and_Effective_Care
head(Timely_and_Effective_Care,10)## # A tibble: 10 × 16
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 9 more variables: TelephoneNumber <chr>, Condition <chr>, MeasureId <chr>,
## # MeasureName <chr>, Score <chr>, Sample <chr>, Footnote <chr>,
## # StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Timely_and_Effective_Care %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## named list()Replacing NA values
# Replacing all "Not Applicable" with NA
Timely_and_Effective_Care <- as.data.frame(sapply(Timely_and_Effective_Care, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Timely_and_Effective_Care <- as.data.frame(sapply(Timely_and_Effective_Care, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Pivoting the data wider
careClean <- Timely_and_Effective_Care %>%
pivot_wider(
names_from = MeasureId,
values_from = c(Score),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(careClean)## [1] 4677 26head(careClean)## # A tibble: 6 × 26
## FacilityName FacilityId State EDV ED_2_Strata_1 ED_2_Strata_2 HCP_COVID_19
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEA… 010001 AL high <NA> <NA> 80.7
## 2 MARSHALL MEDI… 010005 AL high 148 105 79.8
## 3 NORTH ALABAMA… 010006 AL high <NA> <NA> 79
## 4 MIZELL MEMORI… 010007 AL low <NA> <NA> 57.9
## 5 CRENSHAW COMM… 010008 AL low <NA> <NA> 81.2
## 6 ST. VINCENT'S… 010011 AL high <NA> <NA> 88
## # ℹ 19 more variables: IMM_3 <chr>, OP_18b <chr>, OP_18c <chr>, OP_22 <chr>,
## # OP_23 <chr>, OP_29 <chr>, OP_31 <chr>, SAFE_USE_OF_OPIOIDS <chr>,
## # SEP_1 <chr>, SEP_SH_3HR <chr>, SEP_SH_6HR <chr>, SEV_SEP_3HR <chr>,
## # SEV_SEP_6HR <chr>, STK_02 <chr>, STK_03 <chr>, STK_05 <chr>, STK_06 <chr>,
## # VTE_1 <chr>, VTE_2 <chr>Exploring and Preprocessing the Complications_and_Deaths dataset
Viewing and checking for missing values
# Display first 10 rows of Complications_and_Deaths
head(Complications_and_Deaths,10)## # A tibble: 10 × 18
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 6 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 7 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 8 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 9 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 10 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## # ℹ 11 more variables: TelephoneNumber <chr>, MeasureId <chr>,
## # MeasureName <chr>, ComparedToNational <chr>, Denominator <chr>,
## # Score <chr>, LowerEstimate <chr>, HigherEstimate <chr>, Footnote <chr>,
## # StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Complications_and_Deaths %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## named list()Replacing NA values
# Replacing all "Not Applicable" with NA
Complications_and_Deaths <- as.data.frame(sapply(Complications_and_Deaths, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Complications_and_Deaths <- as.data.frame(sapply(Complications_and_Deaths, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Pivoting the data wider
deathsClean <- Complications_and_Deaths %>%
pivot_wider(
names_from = MeasureId,
values_from = c(ComparedToNational, Score),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(deathsClean)## [1] 4814 41head(deathsClean)## # A tibble: 6 × 41
## FacilityName FacilityId State ComparedToNational_C…¹ ComparedToNational_M…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL No Different Than the… No Different Than the…
## 2 MARSHALL MEDIC… 010005 AL No Different Than the… No Different Than the…
## 3 NORTH ALABAMA … 010006 AL No Different Than the… Worse Than the Nation…
## 4 MIZELL MEMORIA… 010007 AL Number of Cases Too S… Number of Cases Too S…
## 5 CRENSHAW COMMU… 010008 AL <NA> Number of Cases Too S…
## 6 ST. VINCENT'S … 010011 AL No Different Than the… No Different Than the…
## # ℹ abbreviated names: ¹ComparedToNational_COMP_HIP_KNEE,
## # ²ComparedToNational_MORT_30_AMI
## # ℹ 36 more variables: ComparedToNational_MORT_30_CABG <chr>,
## # ComparedToNational_MORT_30_COPD <chr>, ComparedToNational_MORT_30_HF <chr>,
## # ComparedToNational_MORT_30_PN <chr>, ComparedToNational_MORT_30_STK <chr>,
## # ComparedToNational_PSI_03 <chr>, ComparedToNational_PSI_04 <chr>,
## # ComparedToNational_PSI_06 <chr>, ComparedToNational_PSI_08 <chr>, …Exploring and Preprocessing the Payment_and_Value_of_Care dataset
Viewing and checking for missing values
# Display first 10 rows of Payment_and_Value_of_Care
head(Payment_and_Value_of_Care,10)## # A tibble: 10 × 22
## FacilityId FacilityName Address CityTown State ZipCode CountyParish
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 2 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 3 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 4 010001 SOUTHEAST HEALTH MEDI… 1108 R… DOTHAN AL 36301 HOUSTON
## 5 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 6 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 7 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 8 010005 MARSHALL MEDICAL CENT… 2505 U… BOAZ AL 35957 MARSHALL
## 9 010006 NORTH ALABAMA MEDICAL… 1701 V… FLORENCE AL 35630 LAUDERDALE
## 10 010006 NORTH ALABAMA MEDICAL… 1701 V… FLORENCE AL 35630 LAUDERDALE
## # ℹ 15 more variables: TelephoneNumber <chr>, PaymentMeasureId <chr>,
## # PaymentMeasureName <chr>, PaymentCategory <chr>, Denominator <chr>,
## # Payment <chr>, LowerEstimate <chr>, HigherEstimate <chr>,
## # PaymentFootnote <dbl>, ValueOfCareDisplayId <chr>,
## # ValueOfCareDisplayName <chr>, ValueOfCareCategory <chr>,
## # ValueOfCareFootnote <dbl>, StartDate <chr>, EndDate <chr># Filter dataset to include numeric columns only
num_vars <- Payment_and_Value_of_Care %>%
select_if(is.numeric)
# Check for missing values
miss_vals <- sapply(num_vars, function(x) sum(is.na(x)))
print(miss_vals)## PaymentFootnote ValueOfCareFootnote
## 9956 10044Replacing NA values
# Replacing all "Not Applicable" with NA
Payment_and_Value_of_Care <- as.data.frame(sapply(Payment_and_Value_of_Care, function(x) {
if (is.character(x)) {
x[x == "Not Applicable"] <- NA
}
return(x)
}))
# Replacing all "Not Available" with NA
Payment_and_Value_of_Care <- as.data.frame(sapply(Payment_and_Value_of_Care, function(x) {
if (is.character(x)) {
x[x == "Not Available"] <- NA
}
return(x)
}))Pivoting the data wider
paymentClean <- Payment_and_Value_of_Care %>%
pivot_wider(
names_from = PaymentMeasureId,
values_from = c(PaymentCategory, Payment),
id_cols = c(FacilityName, FacilityId, State)
)
# Check the new dataframe
dim(paymentClean)## [1] 4645 11head(paymentClean)## # A tibble: 6 × 11
## FacilityName FacilityId State PaymentCategory_PAYM…¹ PaymentCategory_PAYM…²
## <chr> <chr> <chr> <chr> <chr>
## 1 SOUTHEAST HEAL… 010001 AL No Different Than the… No Different Than the…
## 2 MARSHALL MEDIC… 010005 AL No Different Than the… No Different Than the…
## 3 NORTH ALABAMA … 010006 AL Greater Than the Nati… No Different Than the…
## 4 MIZELL MEMORIA… 010007 AL Number of Cases Too S… No Different Than the…
## 5 CRENSHAW COMMU… 010008 AL Number of Cases Too S… Number of Cases Too S…
## 6 ST. VINCENT'S … 010011 AL No Different Than the… No Different Than the…
## # ℹ abbreviated names: ¹PaymentCategory_PAYM_30_AMI,
## # ²PaymentCategory_PAYM_30_HF
## # ℹ 6 more variables: PaymentCategory_PAYM_30_PN <chr>,
## # PaymentCategory_PAYM_90_HIP_KNEE <chr>, Payment_PAYM_30_AMI <chr>,
## # Payment_PAYM_30_HF <chr>, Payment_PAYM_30_PN <chr>,
## # Payment_PAYM_90_HIP_KNEE <chr>Joining and cleaning the datasets
Joining the datasets based on FacilityId
HipKneeCleanTest <- readmissionsClean %>%
full_join(HCAHPSClean, by = "FacilityId") %>%
full_join(careClean, by = "FacilityId") %>%
full_join(deathsClean, by = "FacilityId") %>%
full_join(paymentClean, by = "FacilityId")
head(HipKneeCleanTest)## # A tibble: 6 × 451
## FacilityName.x FacilityId State.x NumberOfDischarges_HIP-KN…¹
## <chr> <chr> <chr> <dbl>
## 1 SOUTHEAST HEALTH MEDICAL CENTER 010001 AL NA
## 2 MARSHALL MEDICAL CENTERS 010005 AL NA
## 3 NORTH ALABAMA MEDICAL CENTER 010006 AL NA
## 4 MIZELL MEMORIAL HOSPITAL 010007 AL NA
## 5 CRENSHAW COMMUNITY HOSPITAL 010008 AL NA
## 6 ST. VINCENT'S EAST 010011 AL NA
## # ℹ abbreviated name: ¹`NumberOfDischarges_HIP-KNEE`
## # ℹ 447 more variables: `ExcessReadmissionRatio_HIP-KNEE` <dbl>,
## # `PredictedReadmissionRate_HIP-KNEE` <dbl>,
## # `ExpectedReadmissionRate_HIP-KNEE` <dbl>,
## # `NumberOfReadmissions_HIP-KNEE` <dbl>, FacilityName.y <chr>, State.y <chr>,
## # PatientSurveyStarRating_H_COMP_1_A_P <chr>,
## # PatientSurveyStarRating_H_COMP_1_SN_P <chr>, …Removing redundant columns
# Removing duplicate columns
HipKneeCleanTest <- HipKneeCleanTest %>%
select(-matches("\\.(x|y|z|w|v)$"))Checking for NA Values
# Checking the dimensions
dim(HipKneeCleanTest)
# Count NA values in each column
na_counts <- sapply(HipKneeCleanTest, function(x) sum(is.na(x)))
# View the NA counts
print(na_counts)Removing columns with more than 80% NA values
# Calculate the percentage of NA values for each column
na_percentage <- sapply(HipKneeCleanTest, function(x) mean(is.na(x)))
# Remove columns where more than 80% of the values are NA
HipKneeCleanTest <- HipKneeCleanTest[, na_percentage <= 0.8]
# Count NA values in each column
na_counts <- sapply(HipKneeCleanTest, function(x) sum(is.na(x)))
# View the NA counts
print(na_counts)
# Check the dimensions
dim(HipKneeCleanTest)Removing answer percent and survey response percent columns
# Remove columns containing 'AnswerPercent' or 'SurveyResponseRate'
HipKneeCleanTest <- HipKneeCleanTest %>%
select(-matches("AnswerPercent|SurveyResponseRate"))
# Check the dimensions
dim(HipKneeCleanTest)## [1] 4816 87Removing compared to national columns
# Remove columns containing 'ComparedToNational' and 'PaymentCategory'
HipKneeCleanTest <- HipKneeCleanTest %>%
select(-matches("ComparedToNational|PaymentCategory"))
# Check the dimensions
dim(HipKneeCleanTest)## [1] 4816 67Checking data structure
str(HipKneeCleanTest)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 3 1 7 5 NA 9 2 10 NA 1 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : chr [1:4816] "3" "3" "2" "3" ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : chr [1:4816] "4" "4" "3" "5" ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : chr [1:4816] "3" "2" "2" "4" ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : chr [1:4816] "3" "3" "2" "3" ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : chr [1:4816] "4" "3" "3" "4" ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : chr [1:4816] "3" "2" "1" "2" ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : chr [1:4816] "4" "4" "4" "4" ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ PatientSurveyStarRating_H_STAR_RATING : chr [1:4816] "4" "3" "2" "4" ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : chr [1:4816] "89" "90" "88" "91" ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : chr [1:4816] "91" "92" "89" "95" ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : chr [1:4816] "81" "75" "75" "88" ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : chr [1:4816] "77" "76" "71" "77" ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : chr [1:4816] "87" "86" "83" "87" ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : chr [1:4816] "82" "79" "77" "82" ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : chr [1:4816] "84" "80" "74" "80" ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : chr [1:4816] "86" "85" "85" "87" ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : chr [1:4816] "89" "85" "82" "89" ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : chr [1:4816] "90" "83" "79" "88" ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : chr [1:4816] NA "148" NA NA ...
## $ HCP_COVID_19 : chr [1:4816] "80.7" "79.8" "79" "57.9" ...
## $ IMM_3 : chr [1:4816] "95" "80" "67" "53" ...
## $ OP_18b : chr [1:4816] "215" "147" "177" "130" ...
## $ OP_18c : chr [1:4816] "317" "266" NA "216" ...
## $ OP_22 : chr [1:4816] "5" "3" "1" "4" ...
## $ OP_23 : chr [1:4816] NA NA "69" NA ...
## $ OP_29 : chr [1:4816] "47" "96" "85" "23" ...
## $ SAFE_USE_OF_OPIOIDS : chr [1:4816] "14" "19" "17" NA ...
## $ SEP_1 : chr [1:4816] "66" "74" "56" "86" ...
## $ SEP_SH_3HR : chr [1:4816] "70" "88" "77" NA ...
## $ SEP_SH_6HR : chr [1:4816] "100" "91" "81" NA ...
## $ SEV_SEP_3HR : chr [1:4816] "79" "88" "78" "89" ...
## $ SEV_SEP_6HR : chr [1:4816] "95" "96" "86" "97" ...
## $ STK_02 : chr [1:4816] "98" "100" "96" NA ...
## $ STK_05 : chr [1:4816] NA "91" NA NA ...
## $ STK_06 : chr [1:4816] NA NA "97" NA ...
## $ VTE_1 : chr [1:4816] "98" NA NA NA ...
## $ VTE_2 : chr [1:4816] "99" NA "97" NA ...
## $ Score_COMP_HIP_KNEE : chr [1:4816] "2.7" "2.3" "4.6" NA ...
## $ Score_MORT_30_AMI : chr [1:4816] "12" "13.6" "16.5" NA ...
## $ Score_MORT_30_COPD : chr [1:4816] "8.8" "9.9" "9.9" "13.7" ...
## $ Score_MORT_30_HF : chr [1:4816] "8.9" "14.9" "12.5" "12.5" ...
## $ Score_MORT_30_PN : chr [1:4816] "18" "23.3" "19.5" "28.5" ...
## $ Score_MORT_30_STK : chr [1:4816] "14.8" "15.3" "17.2" NA ...
## $ Score_PSI_03 : chr [1:4816] "0.39" "0.94" "1.39" "0.42" ...
## $ Score_PSI_04 : chr [1:4816] "184.68" "183.49" "173.63" NA ...
## $ Score_PSI_06 : chr [1:4816] "0.23" "0.22" "0.36" "0.24" ...
## $ Score_PSI_08 : chr [1:4816] "0.10" "0.09" "0.08" "0.09" ...
## $ Score_PSI_09 : chr [1:4816] "2.39" "2.69" "5.43" "2.49" ...
## $ Score_PSI_10 : chr [1:4816] "1.14" "1.37" "1.26" "1.57" ...
## $ Score_PSI_11 : chr [1:4816] "13.83" "7.19" "7.37" "8.45" ...
## $ Score_PSI_12 : chr [1:4816] "4.49" "3.01" "3.36" "3.89" ...
## $ Score_PSI_13 : chr [1:4816] "8.05" "4.46" "4.37" "5.19" ...
## $ Score_PSI_14 : chr [1:4816] "1.69" "1.87" "1.76" NA ...
## $ Score_PSI_15 : chr [1:4816] "0.93" "0.91" "1.34" "1.08" ...
## $ Score_PSI_90 : chr [1:4816] "1.21" "0.97" "1.17" "0.95" ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : chr [1:4816] "$22,212" "$18,030" "$21,898" NA ...# Convert columns to numeric
HipKneeCleanTest <- HipKneeCleanTest %>%
mutate_at(vars(starts_with("PatientSurveyStarRating_"),
starts_with("HcahpsLinearMeanValue_"),
starts_with("Score_"),
starts_with("ED_"),
starts_with("IMM_"),
starts_with("OP_"),
starts_with("SEP_"),
starts_with("SEV_"),
starts_with("STK_"),
starts_with("VTE_"),
starts_with("SAFE_"),
starts_with("HCP_")),
~ as.numeric(as.character(.)))
# View the structure
str(HipKneeCleanTest)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 3 1 7 5 NA 9 2 10 NA 1 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : num [1:4816] 4 4 3 5 NA 3 4 4 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : num [1:4816] 3 2 2 4 NA 4 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : num [1:4816] 4 3 3 4 NA 3 3 2 NA 3 ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : num [1:4816] 3 2 1 2 NA 2 2 1 NA 4 ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : num [1:4816] 4 4 4 4 NA 4 4 3 NA 5 ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: num [1:4816] 4 3 2 4 NA 3 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : num [1:4816] 4 3 2 4 NA 4 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : num [1:4816] 89 90 88 91 NA 90 91 89 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : num [1:4816] 91 92 89 95 NA 90 91 91 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : num [1:4816] 81 75 75 88 NA 85 80 78 NA 85 ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : num [1:4816] 77 76 71 77 NA 76 76 72 NA 78 ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : num [1:4816] 87 86 83 87 NA 86 86 81 NA 86 ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : num [1:4816] 82 79 77 82 NA 81 79 80 NA 83 ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : num [1:4816] 84 80 74 80 NA 81 83 78 NA 88 ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : num [1:4816] 86 85 85 87 NA 84 84 82 NA 89 ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : num [1:4816] 89 85 82 89 NA 88 83 85 NA 90 ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : num [1:4816] 90 83 79 88 NA 87 80 84 NA 91 ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : num [1:4816] NA 148 NA NA NA NA NA NA NA NA ...
## $ HCP_COVID_19 : num [1:4816] 80.7 79.8 79 57.9 81.2 88 69.8 87.3 95.9 85.3 ...
## $ IMM_3 : num [1:4816] 95 80 67 53 45 81 65 93 98 81 ...
## $ OP_18b : num [1:4816] 215 147 177 130 118 206 160 185 102 145 ...
## $ OP_18c : num [1:4816] 317 266 NA 216 98 124 220 220 NA 324 ...
## $ OP_22 : num [1:4816] 5 3 1 4 0 5 4 3 0 2 ...
## $ OP_23 : num [1:4816] NA NA 69 NA NA 47 NA 73 NA 35 ...
## $ OP_29 : num [1:4816] 47 96 85 23 67 100 100 NA NA 82 ...
## $ SAFE_USE_OF_OPIOIDS : num [1:4816] 14 19 17 NA NA 20 14 23 NA 17 ...
## $ SEP_1 : num [1:4816] 66 74 56 86 NA 51 92 77 NA 87 ...
## $ SEP_SH_3HR : num [1:4816] 70 88 77 NA NA 78 94 83 NA 90 ...
## $ SEP_SH_6HR : num [1:4816] 100 91 81 NA NA 81 83 100 NA 94 ...
## $ SEV_SEP_3HR : num [1:4816] 79 88 78 89 NA 69 95 85 NA 94 ...
## $ SEV_SEP_6HR : num [1:4816] 95 96 86 97 NA 91 99 97 NA 99 ...
## $ STK_02 : num [1:4816] 98 100 96 NA NA 93 NA 99 NA NA ...
## $ STK_05 : num [1:4816] NA 91 NA NA NA NA NA NA NA NA ...
## $ STK_06 : num [1:4816] NA NA 97 NA NA NA NA NA NA NA ...
## $ VTE_1 : num [1:4816] 98 NA NA NA NA 79 89 84 44 59 ...
## $ VTE_2 : num [1:4816] 99 NA 97 NA NA 88 93 94 NA NA ...
## $ Score_COMP_HIP_KNEE : num [1:4816] 2.7 2.3 4.6 NA NA 3.5 3.8 3.5 NA 4.3 ...
## $ Score_MORT_30_AMI : num [1:4816] 12 13.6 16.5 NA NA 13.2 13.8 13.1 NA NA ...
## $ Score_MORT_30_COPD : num [1:4816] 8.8 9.9 9.9 13.7 NA 10.3 NA 9.2 NA 7.8 ...
## $ Score_MORT_30_HF : num [1:4816] 8.9 14.9 12.5 12.5 NA 13.5 13.6 9.9 NA 16.9 ...
## $ Score_MORT_30_PN : num [1:4816] 18 23.3 19.5 28.5 NA 20.9 22 17.2 NA 26.1 ...
## $ Score_MORT_30_STK : num [1:4816] 14.8 15.3 17.2 NA NA 12.3 NA 13.2 NA 17.3 ...
## $ Score_PSI_03 : num [1:4816] 0.39 0.94 1.39 0.42 0.54 0.13 0.41 0.63 0.57 0.47 ...
## $ Score_PSI_04 : num [1:4816] 185 183 174 NA NA ...
## $ Score_PSI_06 : num [1:4816] 0.23 0.22 0.36 0.24 0.25 0.24 0.24 0.21 0.25 0.22 ...
## $ Score_PSI_08 : num [1:4816] 0.1 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 ...
## $ Score_PSI_09 : num [1:4816] 2.39 2.69 5.43 2.49 NA 1.88 2.44 3.29 2.44 2.58 ...
## $ Score_PSI_10 : num [1:4816] 1.14 1.37 1.26 1.57 NA 1.72 1.51 1.2 1.57 NA ...
## $ Score_PSI_11 : num [1:4816] 13.83 7.19 7.37 8.45 NA ...
## $ Score_PSI_12 : num [1:4816] 4.49 3.01 3.36 3.89 NA 3.04 3.32 3.67 3.56 5.63 ...
## $ Score_PSI_13 : num [1:4816] 8.05 4.46 4.37 5.19 NA 5.55 4.88 6.08 5.18 NA ...
## $ Score_PSI_14 : num [1:4816] 1.69 1.87 1.76 NA NA 1.86 2.46 2.77 NA 1.83 ...
## $ Score_PSI_15 : num [1:4816] 0.93 0.91 1.34 1.08 NA 1.18 1.04 0.84 NA 0.88 ...
## $ Score_PSI_90 : num [1:4816] 1.21 0.97 1.17 0.95 NA 0.72 0.89 1.17 0.98 1.05 ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : chr [1:4816] "$22,212" "$18,030" "$21,898" NA ...Fixing the payment column
# Remove $ and , and convert to numeric
HipKneeCleanTest <- HipKneeCleanTest %>%
mutate_at(vars(starts_with("Payment_")),
~ as.numeric(gsub("[\\$,]", "", .)))
# Checking the structure
str(HipKneeCleanTest)## tibble [4,816 × 67] (S3: tbl_df/tbl/data.frame)
## $ FacilityId : chr [1:4816] "010001" "010005" "010006" "010007" ...
## $ ExcessReadmissionRatio_HIP-KNEE : num [1:4816] 0.892 0.798 1.247 0.992 NA ...
## $ PredictedReadmissionRate_HIP-KNEE : num [1:4816] 3.53 3.76 5.52 4.34 NA ...
## $ ExpectedReadmissionRate_HIP-KNEE : num [1:4816] 3.96 4.72 4.43 4.37 NA ...
## $ NumberOfReadmissions_HIP-KNEE : num [1:4816] 3 1 7 5 NA 9 2 10 NA 1 ...
## $ PatientSurveyStarRating_H_COMP_1_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_2_STAR_RATING : num [1:4816] 4 4 3 5 NA 3 4 4 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_3_STAR_RATING : num [1:4816] 3 2 2 4 NA 4 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_5_STAR_RATING : num [1:4816] 3 3 2 3 NA 3 3 2 NA 4 ...
## $ PatientSurveyStarRating_H_COMP_6_STAR_RATING : num [1:4816] 4 3 3 4 NA 3 3 2 NA 3 ...
## $ PatientSurveyStarRating_H_COMP_7_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ PatientSurveyStarRating_H_CLEAN_STAR_RATING : num [1:4816] 3 2 1 2 NA 2 2 1 NA 4 ...
## $ PatientSurveyStarRating_H_QUIET_STAR_RATING : num [1:4816] 4 4 4 4 NA 4 4 3 NA 5 ...
## $ PatientSurveyStarRating_H_HSP_RATING_STAR_RATING: num [1:4816] 4 3 2 4 NA 3 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_RECMND_STAR_RATING : num [1:4816] 4 3 2 4 NA 4 2 3 NA 4 ...
## $ PatientSurveyStarRating_H_STAR_RATING : num [1:4816] 4 3 2 4 NA 3 3 3 NA 4 ...
## $ HcahpsLinearMeanValue_H_COMP_1_LINEAR_SCORE : num [1:4816] 89 90 88 91 NA 90 91 89 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_2_LINEAR_SCORE : num [1:4816] 91 92 89 95 NA 90 91 91 NA 92 ...
## $ HcahpsLinearMeanValue_H_COMP_3_LINEAR_SCORE : num [1:4816] 81 75 75 88 NA 85 80 78 NA 85 ...
## $ HcahpsLinearMeanValue_H_COMP_5_LINEAR_SCORE : num [1:4816] 77 76 71 77 NA 76 76 72 NA 78 ...
## $ HcahpsLinearMeanValue_H_COMP_6_LINEAR_SCORE : num [1:4816] 87 86 83 87 NA 86 86 81 NA 86 ...
## $ HcahpsLinearMeanValue_H_COMP_7_LINEAR_SCORE : num [1:4816] 82 79 77 82 NA 81 79 80 NA 83 ...
## $ HcahpsLinearMeanValue_H_CLEAN_LINEAR_SCORE : num [1:4816] 84 80 74 80 NA 81 83 78 NA 88 ...
## $ HcahpsLinearMeanValue_H_QUIET_LINEAR_SCORE : num [1:4816] 86 85 85 87 NA 84 84 82 NA 89 ...
## $ HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE : num [1:4816] 89 85 82 89 NA 88 83 85 NA 90 ...
## $ HcahpsLinearMeanValue_H_RECMND_LINEAR_SCORE : num [1:4816] 90 83 79 88 NA 87 80 84 NA 91 ...
## $ EDV : chr [1:4816] "high" "high" "high" "low" ...
## $ ED_2_Strata_1 : num [1:4816] NA 148 NA NA NA NA NA NA NA NA ...
## $ HCP_COVID_19 : num [1:4816] 80.7 79.8 79 57.9 81.2 88 69.8 87.3 95.9 85.3 ...
## $ IMM_3 : num [1:4816] 95 80 67 53 45 81 65 93 98 81 ...
## $ OP_18b : num [1:4816] 215 147 177 130 118 206 160 185 102 145 ...
## $ OP_18c : num [1:4816] 317 266 NA 216 98 124 220 220 NA 324 ...
## $ OP_22 : num [1:4816] 5 3 1 4 0 5 4 3 0 2 ...
## $ OP_23 : num [1:4816] NA NA 69 NA NA 47 NA 73 NA 35 ...
## $ OP_29 : num [1:4816] 47 96 85 23 67 100 100 NA NA 82 ...
## $ SAFE_USE_OF_OPIOIDS : num [1:4816] 14 19 17 NA NA 20 14 23 NA 17 ...
## $ SEP_1 : num [1:4816] 66 74 56 86 NA 51 92 77 NA 87 ...
## $ SEP_SH_3HR : num [1:4816] 70 88 77 NA NA 78 94 83 NA 90 ...
## $ SEP_SH_6HR : num [1:4816] 100 91 81 NA NA 81 83 100 NA 94 ...
## $ SEV_SEP_3HR : num [1:4816] 79 88 78 89 NA 69 95 85 NA 94 ...
## $ SEV_SEP_6HR : num [1:4816] 95 96 86 97 NA 91 99 97 NA 99 ...
## $ STK_02 : num [1:4816] 98 100 96 NA NA 93 NA 99 NA NA ...
## $ STK_05 : num [1:4816] NA 91 NA NA NA NA NA NA NA NA ...
## $ STK_06 : num [1:4816] NA NA 97 NA NA NA NA NA NA NA ...
## $ VTE_1 : num [1:4816] 98 NA NA NA NA 79 89 84 44 59 ...
## $ VTE_2 : num [1:4816] 99 NA 97 NA NA 88 93 94 NA NA ...
## $ Score_COMP_HIP_KNEE : num [1:4816] 2.7 2.3 4.6 NA NA 3.5 3.8 3.5 NA 4.3 ...
## $ Score_MORT_30_AMI : num [1:4816] 12 13.6 16.5 NA NA 13.2 13.8 13.1 NA NA ...
## $ Score_MORT_30_COPD : num [1:4816] 8.8 9.9 9.9 13.7 NA 10.3 NA 9.2 NA 7.8 ...
## $ Score_MORT_30_HF : num [1:4816] 8.9 14.9 12.5 12.5 NA 13.5 13.6 9.9 NA 16.9 ...
## $ Score_MORT_30_PN : num [1:4816] 18 23.3 19.5 28.5 NA 20.9 22 17.2 NA 26.1 ...
## $ Score_MORT_30_STK : num [1:4816] 14.8 15.3 17.2 NA NA 12.3 NA 13.2 NA 17.3 ...
## $ Score_PSI_03 : num [1:4816] 0.39 0.94 1.39 0.42 0.54 0.13 0.41 0.63 0.57 0.47 ...
## $ Score_PSI_04 : num [1:4816] 185 183 174 NA NA ...
## $ Score_PSI_06 : num [1:4816] 0.23 0.22 0.36 0.24 0.25 0.24 0.24 0.21 0.25 0.22 ...
## $ Score_PSI_08 : num [1:4816] 0.1 0.09 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 ...
## $ Score_PSI_09 : num [1:4816] 2.39 2.69 5.43 2.49 NA 1.88 2.44 3.29 2.44 2.58 ...
## $ Score_PSI_10 : num [1:4816] 1.14 1.37 1.26 1.57 NA 1.72 1.51 1.2 1.57 NA ...
## $ Score_PSI_11 : num [1:4816] 13.83 7.19 7.37 8.45 NA ...
## $ Score_PSI_12 : num [1:4816] 4.49 3.01 3.36 3.89 NA 3.04 3.32 3.67 3.56 5.63 ...
## $ Score_PSI_13 : num [1:4816] 8.05 4.46 4.37 5.19 NA 5.55 4.88 6.08 5.18 NA ...
## $ Score_PSI_14 : num [1:4816] 1.69 1.87 1.76 NA NA 1.86 2.46 2.77 NA 1.83 ...
## $ Score_PSI_15 : num [1:4816] 0.93 0.91 1.34 1.08 NA 1.18 1.04 0.84 NA 0.88 ...
## $ Score_PSI_90 : num [1:4816] 1.21 0.97 1.17 0.95 NA 0.72 0.89 1.17 0.98 1.05 ...
## $ FacilityName : chr [1:4816] "SOUTHEAST HEALTH MEDICAL CENTER" "MARSHALL MEDICAL CENTERS" "NORTH ALABAMA MEDICAL CENTER" "MIZELL MEMORIAL HOSPITAL" ...
## $ State : chr [1:4816] "AL" "AL" "AL" "AL" ...
## $ Payment_PAYM_90_HIP_KNEE : num [1:4816] 22212 18030 21898 NA NA ...Encoding categorical variables
Identify Categorical Variables
# Create function to find categorical variables
is_categorical <- function(x) is.factor(x) | is.character(x)
# Apply function to all variables in the dataset
categorical_vars <- sapply(HipKneeClean, is_categorical)
# Print the names of all categorical variables
categorical <- names(HipKneeClean)[categorical_vars]
categorical## [1] "FacilityId" "EDV" "FacilityName" "State"Dummy encode the EDV column
# Define the encoding mapping (ignore NAs for now)
encoding_map <- c(
'low' = 1,
'medium' = 2,
'high' = 3,
'very high' = 4
)
# Dummy encoding used due to ordinal nature of this data
# Create a copy of HipKneeCleanTest and name it HipKneeTest to separate cleaned dataset and the test dataset
HipKneeTest <- HipKneeCleanTest %>%
mutate(EDV = recode(EDV, !!!encoding_map))
# Print first 20 rows of EDV column in HipKneeClean and HipKneeTrain to ensure proper encoding
cat("HipKneeCleanTest")## HipKneeCleanTestprint(head(HipKneeCleanTest$EDV, 20))## [1] "high" "high" "high" "low" "low" "high"
## [7] "low" "medium" "low" "medium" "low" "low"
## [13] "high" "high" "very high" "very high" "low" "high"
## [19] "low" "very high"cat("HipKneeTest")## HipKneeTestprint(head(HipKneeTest$EDV, 20))## [1] 3 3 3 1 1 3 1 2 1 2 1 1 3 3 4 4 1 3 1 4Encode each state in alphabetical order
# Manually map out each state with their respective code in alphabetical order with a preceding 0 to make data non-ordinal
state_mapping <- c(
"AL" = "001",
"AK" = "002",
"AZ" = "003",
"AR" = "004",
"CA" = "005",
"CO" = "006",
"CT" = "007",
"DE" = "008",
"FL" = "009",
"GA" = "010",
"HI" = "011",
"ID" = "012",
"IL" = "013",
"IN" = "014",
"IA" = "015",
"KS" = "016",
"KY" = "017",
"LA" = "018",
"ME" = "019",
"MD" = "020",
"MA" = "021",
"MI" = "022",
"MN" = "023",
"MS" = "024",
"MO" = "025",
"MT" = "026",
"NE" = "027",
"NV" = "028",
"NH" = "029",
"NJ" = "030",
"NM" = "031",
"NY" = "032",
"NC" = "033",
"ND" = "034",
"OH" = "035",
"OK" = "036",
"OR" = "037",
"PA" = "038",
"RI" = "039",
"SC" = "040",
"SD" = "041",
"TN" = "042",
"TX" = "043",
"UT" = "044",
"VT" = "045",
"VA" = "046",
"WA" = "047",
"WV" = "048",
"WI" = "049",
"WY" = "050"
)
# Create new "StateCode" column with the encoded values
HipKneeTest <- HipKneeTest %>%
mutate(StateCode = state_mapping[State])
# Print 100 rows of the "State" and "StateCode" columns to ensure accuracy
print("State and StateCode Columns")## [1] "State and StateCode Columns"print(head(HipKneeTest[c("State", "StateCode")], 100))## # A tibble: 100 × 2
## State StateCode
## <chr> <chr>
## 1 AL 001
## 2 AL 001
## 3 AL 001
## 4 AL 001
## 5 AL 001
## 6 AL 001
## 7 AL 001
## 8 AL 001
## 9 AL 001
## 10 AL 001
## # ℹ 90 more rows# Print all unique values in "StateCode" column to ensure accuracy
print("Unique StateCode Values")## [1] "Unique StateCode Values"print(unique(HipKneeTest$StateCode))## [1] "001" "002" "003" "004" "005" "006" "007" "008" NA "009" "010" "011"
## [13] "012" "013" "014" "015" "016" "017" "018" "019" "020" "021" "022" "023"
## [25] "024" "025" "026" "027" "028" "029" "030" "031" "032" "033" "034" "035"
## [37] "036" "037" "038" "039" "040" "041" "042" "043" "044" "045" "046" "047"
## [49] "048" "049" "050"Collinearity and Feature Removal
Reassess collinearity with heatmap and correlation matrix
# Compute correlation matrix
cor_matrix <- cor(HipKneeTest %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
# Convert correlation matrix to df
cor_table <- as.data.frame(cor_matrix)
# Add variable names as a column
cor_table$Variable <- rownames(cor_table)
# Reorder columns
cor_table <- cor_table %>%
select(Variable, everything())
# Print table
cor_table %>%
kable(caption = "Table 8. Correlation Coefficients Table") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | PredictedReadmissionRate_HIP-KNEE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | EDV | HCP_COVID_19 | IMM_3 | OP_18b | OP_29 | SAFE_USE_OF_OPIOIDS | VTE_1 | Score_COMP_HIP_KNEE | Score_MORT_30_AMI | Score_MORT_30_COPD | Score_MORT_30_HF | Score_MORT_30_PN | Score_MORT_30_STK | Score_PSI_03 | Score_PSI_04 | Score_PSI_06 | Score_PSI_08 | Score_PSI_09 | Score_PSI_10 | Score_PSI_11 | Score_PSI_12 | Score_PSI_13 | Score_PSI_14 | Score_PSI_15 | Payment_PAYM_90_HIP_KNEE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PredictedReadmissionRate_HIP-KNEE | PredictedReadmissionRate_HIP-KNEE | 1.0000000 | -0.2060912 | 0.1986939 | -0.0563082 | -0.0028840 | 0.1295727 | -0.0106510 | 0.1063002 | 0.0654668 | 0.3208550 | 0.0074065 | -0.0794948 | -0.1067828 | -0.0985660 | -0.0376746 | -0.0037334 | -0.0449077 | 0.0154891 | -0.0214412 | -0.0182303 | 0.0710046 | 0.1130121 | 0.1047402 | 0.1193336 | 0.0140012 | -0.0158282 | 0.2975679 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | -0.2060912 | 1.0000000 | -0.2262341 | 0.0302154 | 0.2182505 | -0.2448842 | 0.0865028 | 0.1100002 | -0.0248375 | -0.1091775 | -0.0952111 | -0.0230632 | 0.0300940 | -0.0702915 | -0.0760295 | -0.0499063 | -0.0948401 | -0.0054742 | -0.0590998 | 0.0707774 | -0.0157615 | -0.1615833 | -0.0674588 | -0.1458284 | -0.0172231 | 0.0304634 | -0.2108956 |
| EDV | EDV | 0.1986939 | -0.2262341 | 1.0000000 | 0.1599806 | 0.0038674 | 0.5918897 | 0.0603877 | -0.1223889 | 0.2992859 | -0.0240093 | -0.0687401 | -0.0840621 | -0.2588739 | -0.1904351 | -0.0754281 | 0.0292819 | -0.0438108 | -0.0308989 | -0.1637017 | -0.0181523 | 0.0837980 | 0.0455190 | 0.0749218 | 0.0724936 | 0.0399935 | 0.0144400 | 0.0053946 |
| HCP_COVID_19 | HCP_COVID_19 | -0.0563082 | 0.0302154 | 0.1599806 | 1.0000000 | 0.3203622 | 0.2574291 | 0.1067941 | -0.0812735 | 0.0241622 | -0.0510683 | -0.0869890 | -0.1128278 | -0.1245435 | -0.1523779 | -0.0988833 | 0.0943953 | 0.0417007 | 0.0225916 | -0.0272232 | 0.0549990 | 0.0009222 | -0.0909811 | 0.1091949 | -0.0160408 | 0.0169512 | 0.0430812 | -0.0627505 |
| IMM_3 | IMM_3 | -0.0028840 | 0.2182505 | 0.0038674 | 0.3203622 | 1.0000000 | 0.1105628 | 0.1317922 | 0.0410289 | 0.0906329 | -0.0212916 | -0.0165321 | -0.0616051 | -0.0010634 | -0.0761104 | 0.0146397 | 0.0451508 | 0.0601831 | 0.0544576 | -0.0311579 | 0.0899361 | 0.0412713 | -0.0625676 | 0.0594933 | -0.0250714 | 0.0723872 | 0.0625753 | -0.0720431 |
| OP_18b | OP_18b | 0.1295727 | -0.2448842 | 0.5918897 | 0.2574291 | 0.1105628 | 1.0000000 | 0.0506067 | -0.1400845 | 0.2344307 | -0.0293698 | -0.0678837 | -0.1527290 | -0.2195933 | -0.1858291 | -0.0905644 | 0.0583644 | 0.0638412 | 0.0187794 | -0.0806516 | 0.0224833 | 0.0544528 | -0.0076797 | 0.1653812 | 0.0993570 | 0.0676972 | 0.0374530 | -0.0241965 |
| OP_29 | OP_29 | -0.0106510 | 0.0865028 | 0.0603877 | 0.1067941 | 0.1317922 | 0.0506067 | 1.0000000 | -0.0650231 | 0.1567526 | -0.0096464 | -0.0600569 | 0.0081252 | 0.0099705 | -0.0536184 | -0.0251654 | -0.0032584 | 0.0312780 | 0.0059688 | -0.0199006 | 0.0150699 | 0.0331569 | -0.0837910 | -0.0108546 | -0.0087678 | 0.0163779 | 0.0419068 | -0.0815209 |
| SAFE_USE_OF_OPIOIDS | SAFE_USE_OF_OPIOIDS | 0.1063002 | 0.1100002 | -0.1223889 | -0.0812735 | 0.0410289 | -0.1400845 | -0.0650231 | 1.0000000 | -0.0563373 | -0.0081923 | -0.0643353 | -0.0362573 | 0.0171605 | -0.0204107 | -0.0804707 | -0.0287158 | -0.0995591 | -0.0603629 | -0.0017558 | -0.0043396 | -0.0382369 | 0.0137283 | -0.0380145 | -0.0258097 | -0.0166146 | -0.0268805 | -0.0048449 |
| VTE_1 | VTE_1 | 0.0654668 | -0.0248375 | 0.2992859 | 0.0241622 | 0.0906329 | 0.2344307 | 0.1567526 | -0.0563373 | 1.0000000 | -0.0526925 | -0.0493931 | -0.0282911 | -0.1051171 | -0.1710235 | -0.1238916 | -0.0378500 | -0.1180160 | -0.0262166 | -0.0522876 | -0.0577213 | -0.0037803 | -0.0301775 | -0.0317534 | -0.0440578 | 0.0086141 | 0.0326658 | -0.1256363 |
| Score_COMP_HIP_KNEE | Score_COMP_HIP_KNEE | 0.3208550 | -0.1091775 | -0.0240093 | -0.0510683 | -0.0212916 | -0.0293698 | -0.0096464 | -0.0081923 | -0.0526925 | 1.0000000 | 0.0830479 | -0.0203930 | -0.0007242 | 0.0241066 | 0.0211621 | 0.0498557 | 0.0038509 | 0.0505415 | 0.0577776 | 0.0540124 | 0.0813038 | 0.1279724 | 0.1458258 | 0.1334619 | 0.0498603 | 0.0433809 | 0.3410864 |
| Score_MORT_30_AMI | Score_MORT_30_AMI | 0.0074065 | -0.0952111 | -0.0687401 | -0.0869890 | -0.0165321 | -0.0678837 | -0.0600569 | -0.0643353 | -0.0493931 | 0.0830479 | 1.0000000 | 0.2498600 | 0.3407616 | 0.3309425 | 0.2222539 | 0.0415523 | 0.2105379 | 0.0885083 | 0.1010348 | 0.0889343 | 0.1066619 | 0.1037006 | 0.0492328 | 0.0467554 | 0.0454462 | 0.0297688 | 0.0591548 |
| Score_MORT_30_COPD | Score_MORT_30_COPD | -0.0794948 | -0.0230632 | -0.0840621 | -0.1128278 | -0.0616051 | -0.1527290 | 0.0081252 | -0.0362573 | -0.0282911 | -0.0203930 | 0.2498600 | 1.0000000 | 0.3844105 | 0.3710744 | 0.2038243 | -0.0069743 | 0.1713379 | 0.0478268 | 0.0397571 | 0.0429090 | 0.0320669 | 0.0426574 | -0.0532586 | 0.0026944 | 0.0734846 | 0.0340007 | -0.0406696 |
| Score_MORT_30_HF | Score_MORT_30_HF | -0.1067828 | 0.0300940 | -0.2588739 | -0.1245435 | -0.0010634 | -0.2195933 | 0.0099705 | 0.0171605 | -0.1051171 | -0.0007242 | 0.3407616 | 0.3844105 | 1.0000000 | 0.4479367 | 0.3147371 | 0.0371596 | 0.2556384 | 0.0679149 | 0.1051698 | 0.0707269 | 0.0383771 | 0.0362529 | -0.0300702 | -0.0086832 | 0.0647245 | 0.0342374 | -0.0350247 |
| Score_MORT_30_PN | Score_MORT_30_PN | -0.0985660 | -0.0702915 | -0.1904351 | -0.1523779 | -0.0761104 | -0.1858291 | -0.0536184 | -0.0204107 | -0.1710235 | 0.0241066 | 0.3309425 | 0.3710744 | 0.4479367 | 1.0000000 | 0.3042563 | 0.0303815 | 0.2301195 | 0.0543554 | 0.0884315 | 0.0217880 | 0.0237048 | 0.0704445 | 0.0089560 | 0.0393676 | 0.0464407 | 0.0029691 | -0.0062985 |
| Score_MORT_30_STK | Score_MORT_30_STK | -0.0376746 | -0.0760295 | -0.0754281 | -0.0988833 | 0.0146397 | -0.0905644 | -0.0251654 | -0.0804707 | -0.1238916 | 0.0211621 | 0.2222539 | 0.2038243 | 0.3147371 | 0.3042563 | 1.0000000 | 0.0687216 | 0.2380935 | 0.0878847 | 0.1014879 | 0.0674377 | 0.0622532 | 0.0725381 | 0.0474896 | 0.0513975 | 0.0492194 | 0.0625191 | -0.0272101 |
| Score_PSI_03 | Score_PSI_03 | -0.0037334 | -0.0499063 | 0.0292819 | 0.0943953 | 0.0451508 | 0.0583644 | -0.0032584 | -0.0287158 | -0.0378500 | 0.0498557 | 0.0415523 | -0.0069743 | 0.0371596 | 0.0303815 | 0.0687216 | 1.0000000 | 0.1353085 | 0.0601750 | 0.0636661 | 0.1407342 | 0.0386211 | 0.0114365 | 0.1186788 | 0.0298580 | 0.0596798 | 0.0999683 | 0.0086745 |
| Score_PSI_04 | Score_PSI_04 | -0.0449077 | -0.0948401 | -0.0438108 | 0.0417007 | 0.0601831 | 0.0638412 | 0.0312780 | -0.0995591 | -0.1180160 | 0.0038509 | 0.2105379 | 0.1713379 | 0.2556384 | 0.2301195 | 0.2380935 | 0.1353085 | 1.0000000 | 0.0601419 | 0.0870693 | 0.1059485 | 0.0523892 | 0.0649032 | 0.0782559 | 0.0123489 | 0.0652098 | 0.1018205 | -0.0766302 |
| Score_PSI_06 | Score_PSI_06 | 0.0154891 | -0.0054742 | -0.0308989 | 0.0225916 | 0.0544576 | 0.0187794 | 0.0059688 | -0.0603629 | -0.0262166 | 0.0505415 | 0.0885083 | 0.0478268 | 0.0679149 | 0.0543554 | 0.0878847 | 0.0601750 | 0.0601419 | 1.0000000 | 0.0724291 | 0.1014588 | 0.0516246 | 0.0351464 | 0.1431056 | 0.0509831 | 0.0527115 | 0.0910520 | 0.0456525 |
| Score_PSI_08 | Score_PSI_08 | -0.0214412 | -0.0590998 | -0.1637017 | -0.0272232 | -0.0311579 | -0.0806516 | -0.0199006 | -0.0017558 | -0.0522876 | 0.0577776 | 0.1010348 | 0.0397571 | 0.1051698 | 0.0884315 | 0.1014879 | 0.0636661 | 0.0870693 | 0.0724291 | 1.0000000 | 0.0052449 | -0.0360093 | 0.0198090 | 0.0394605 | 0.0093444 | 0.0228045 | 0.0127268 | -0.0041983 |
| Score_PSI_09 | Score_PSI_09 | -0.0182303 | 0.0707774 | -0.0181523 | 0.0549990 | 0.0899361 | 0.0224833 | 0.0150699 | -0.0043396 | -0.0577213 | 0.0540124 | 0.0889343 | 0.0429090 | 0.0707269 | 0.0217880 | 0.0674377 | 0.1407342 | 0.1059485 | 0.1014588 | 0.0052449 | 1.0000000 | 0.0885278 | 0.0680540 | 0.1732337 | 0.0519119 | 0.1207438 | 0.2197254 | -0.0237660 |
| Score_PSI_10 | Score_PSI_10 | 0.0710046 | -0.0157615 | 0.0837980 | 0.0009222 | 0.0412713 | 0.0544528 | 0.0331569 | -0.0382369 | -0.0037803 | 0.0813038 | 0.1066619 | 0.0320669 | 0.0383771 | 0.0237048 | 0.0622532 | 0.0386211 | 0.0523892 | 0.0516246 | -0.0360093 | 0.0885278 | 1.0000000 | 0.1626632 | 0.1079488 | 0.2303938 | 0.0453739 | 0.0830134 | 0.0497447 |
| Score_PSI_11 | Score_PSI_11 | 0.1130121 | -0.1615833 | 0.0455190 | -0.0909811 | -0.0625676 | -0.0076797 | -0.0837910 | 0.0137283 | -0.0301775 | 0.1279724 | 0.1037006 | 0.0426574 | 0.0362529 | 0.0704445 | 0.0725381 | 0.0114365 | 0.0649032 | 0.0351464 | 0.0198090 | 0.0680540 | 0.1626632 | 1.0000000 | 0.1172504 | 0.2506376 | -0.0093577 | 0.0464067 | 0.1441986 |
| Score_PSI_12 | Score_PSI_12 | 0.1047402 | -0.0674588 | 0.0749218 | 0.1091949 | 0.0594933 | 0.1653812 | -0.0108546 | -0.0380145 | -0.0317534 | 0.1458258 | 0.0492328 | -0.0532586 | -0.0300702 | 0.0089560 | 0.0474896 | 0.1186788 | 0.0782559 | 0.1431056 | 0.0394605 | 0.1732337 | 0.1079488 | 0.1172504 | 1.0000000 | 0.1742084 | 0.0522204 | 0.1358951 | 0.0655557 |
| Score_PSI_13 | Score_PSI_13 | 0.1193336 | -0.1458284 | 0.0724936 | -0.0160408 | -0.0250714 | 0.0993570 | -0.0087678 | -0.0258097 | -0.0440578 | 0.1334619 | 0.0467554 | 0.0026944 | -0.0086832 | 0.0393676 | 0.0513975 | 0.0298580 | 0.0123489 | 0.0509831 | 0.0093444 | 0.0519119 | 0.2303938 | 0.2506376 | 0.1742084 | 1.0000000 | 0.0056987 | 0.0878105 | 0.0949467 |
| Score_PSI_14 | Score_PSI_14 | 0.0140012 | -0.0172231 | 0.0399935 | 0.0169512 | 0.0723872 | 0.0676972 | 0.0163779 | -0.0166146 | 0.0086141 | 0.0498603 | 0.0454462 | 0.0734846 | 0.0647245 | 0.0464407 | 0.0492194 | 0.0596798 | 0.0652098 | 0.0527115 | 0.0228045 | 0.1207438 | 0.0453739 | -0.0093577 | 0.0522204 | 0.0056987 | 1.0000000 | 0.1176726 | -0.0181150 |
| Score_PSI_15 | Score_PSI_15 | -0.0158282 | 0.0304634 | 0.0144400 | 0.0430812 | 0.0625753 | 0.0374530 | 0.0419068 | -0.0268805 | 0.0326658 | 0.0433809 | 0.0297688 | 0.0340007 | 0.0342374 | 0.0029691 | 0.0625191 | 0.0999683 | 0.1018205 | 0.0910520 | 0.0127268 | 0.2197254 | 0.0830134 | 0.0464067 | 0.1358951 | 0.0878105 | 0.1176726 | 1.0000000 | -0.0467071 |
| Payment_PAYM_90_HIP_KNEE | Payment_PAYM_90_HIP_KNEE | 0.2975679 | -0.2108956 | 0.0053946 | -0.0627505 | -0.0720431 | -0.0241965 | -0.0815209 | -0.0048449 | -0.1256363 | 0.3410864 | 0.0591548 | -0.0406696 | -0.0350247 | -0.0062985 | -0.0272101 | 0.0086745 | -0.0766302 | 0.0456525 | -0.0041983 | -0.0237660 | 0.0497447 | 0.1441986 | 0.0655557 | 0.0949467 | -0.0181150 | -0.0467071 | 1.0000000 |
Imputation and Handling of Missing Values
# Change - to _ in HIP-KNEE
colnames(HipKneeTest) <- gsub("-", "_", colnames(HipKneeTest))
# Remove all NA values in target variable "PredictedReadmissionRate_HIP_KNEE"
HipKneeTest <- HipKneeTest %>% filter(!is.na(PredictedReadmissionRate_HIP_KNEE))
# Remove all NA values in the "State", "StateCode", and "FacilityName" columns
HipKneeTest <- HipKneeTest %>% drop_na(State, StateCode, FacilityName)
# Print number of remaining variables and observations
dimensions <- dim(HipKneeTest)
cat("Number of variables:", dimensions[2], "\n")## Number of variables: 31cat("Number of observations:", dimensions[1], "\n")## Number of observations: 1833# Calculate missing values
missing_values_summary <- HipKneeTest %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeTest)) * 100)
# Print table
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0.0000000 |
| PredictedReadmissionRate_HIP_KNEE | 0 | 0.0000000 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 33 | 1.8003273 |
| EDV | 90 | 4.9099836 |
| HCP_COVID_19 | 16 | 0.8728860 |
| IMM_3 | 16 | 0.8728860 |
| OP_18b | 75 | 4.0916530 |
| OP_29 | 222 | 12.1112930 |
| SAFE_USE_OF_OPIOIDS | 69 | 3.7643208 |
| VTE_1 | 994 | 54.2280415 |
| Score_COMP_HIP_KNEE | 40 | 2.1822149 |
| Score_MORT_30_AMI | 405 | 22.0949264 |
| Score_MORT_30_COPD | 247 | 13.4751773 |
| Score_MORT_30_HF | 141 | 7.6923077 |
| Score_MORT_30_PN | 125 | 6.8194217 |
| Score_MORT_30_STK | 284 | 15.4937261 |
| Score_PSI_03 | 8 | 0.4364430 |
| Score_PSI_04 | 575 | 31.3693399 |
| Score_PSI_06 | 2 | 0.1091107 |
| Score_PSI_08 | 2 | 0.1091107 |
| Score_PSI_09 | 2 | 0.1091107 |
| Score_PSI_10 | 41 | 2.2367703 |
| Score_PSI_11 | 40 | 2.1822149 |
| Score_PSI_12 | 2 | 0.1091107 |
| Score_PSI_13 | 42 | 2.2913257 |
| Score_PSI_14 | 87 | 4.7463175 |
| Score_PSI_15 | 29 | 1.5821058 |
| FacilityName | 0 | 0.0000000 |
| State | 0 | 0.0000000 |
| Payment_PAYM_90_HIP_KNEE | 42 | 2.2913257 |
| StateCode | 0 | 0.0000000 |
Impute variables with low percentage missingness (<5%) by the median for numeric variables and mode for categorical variables
# Calculate median for columns with <5% missing values
numeric_vars_low_missing <- c("HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE", "EDV", "HCP_COVID_19", "IMM_3", "OP_18b", "SAFE_USE_OF_OPIOIDS", "Score_COMP_HIP_KNEE", "Score_PSI_03", "Score_PSI_06", "Score_PSI_08", "Score_PSI_09", "Score_PSI_10", "Score_PSI_11", "Score_PSI_12", "Score_PSI_13", "Score_PSI_14", "Score_PSI_15", "Payment_PAYM_90_HIP_KNEE")
for (var in numeric_vars_low_missing) {
HipKneeTest[[var]][is.na(HipKneeTest[[var]])] <- median(HipKneeTest[[var]], na.rm = TRUE)
}Impute high percentage missingness variables (>5%) using KNN
# Select high missingness variables for KNN imputation
vars_for_knn <- c("VTE_1", "Score_MORT_30_AMI", "Score_MORT_30_COPD", "Score_MORT_30_HF", "Score_MORT_30_PN", "Score_MORT_30_STK", "Score_PSI_04", "OP_29")
# Perform KNN imputation
HipKneeTest_knn <- kNN(HipKneeTest, variable = vars_for_knn, k = 5)
# Remove columns created by the KNN function
HipKneeTest_knn <- HipKneeTest_knn %>% select(-ends_with("_imp"))
# Update HipKneeTrain with imputed values
HipKneeTest[vars_for_knn] <- HipKneeTest_knn[vars_for_knn]# Calculate missing values
missing_values_summary <- HipKneeTest %>%
summarise(across(everything(), ~ sum(is.na(.)))) %>%
pivot_longer(cols = everything(), names_to = "Variable", values_to = "Missing_Count") %>%
mutate(Missing_Percentage = (Missing_Count / nrow(HipKneeTest)) * 100)
# Print table
missing_values_summary %>%
kable(caption = "Table 7. Missing Values Summary") %>%
kable_styling(bootstrap_options = c("hover", "striped", "responsive"))| Variable | Missing_Count | Missing_Percentage |
|---|---|---|
| FacilityId | 0 | 0 |
| PredictedReadmissionRate_HIP_KNEE | 0 | 0 |
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 0 | 0 |
| EDV | 0 | 0 |
| HCP_COVID_19 | 0 | 0 |
| IMM_3 | 0 | 0 |
| OP_18b | 0 | 0 |
| OP_29 | 0 | 0 |
| SAFE_USE_OF_OPIOIDS | 0 | 0 |
| VTE_1 | 0 | 0 |
| Score_COMP_HIP_KNEE | 0 | 0 |
| Score_MORT_30_AMI | 0 | 0 |
| Score_MORT_30_COPD | 0 | 0 |
| Score_MORT_30_HF | 0 | 0 |
| Score_MORT_30_PN | 0 | 0 |
| Score_MORT_30_STK | 0 | 0 |
| Score_PSI_03 | 0 | 0 |
| Score_PSI_04 | 0 | 0 |
| Score_PSI_06 | 0 | 0 |
| Score_PSI_08 | 0 | 0 |
| Score_PSI_09 | 0 | 0 |
| Score_PSI_10 | 0 | 0 |
| Score_PSI_11 | 0 | 0 |
| Score_PSI_12 | 0 | 0 |
| Score_PSI_13 | 0 | 0 |
| Score_PSI_14 | 0 | 0 |
| Score_PSI_15 | 0 | 0 |
| FacilityName | 0 | 0 |
| State | 0 | 0 |
| Payment_PAYM_90_HIP_KNEE | 0 | 0 |
| StateCode | 0 | 0 |
Feature Engineer Mortality Data
# Average death rates amongst mortality variables and create new column "Score_Ovr_MORT"
HipKneeTest$Score_Ovr_MORT <- rowMeans(HipKneeTest[, c("Score_MORT_30_AMI",
"Score_MORT_30_COPD",
"Score_MORT_30_HF",
"Score_MORT_30_PN",
"Score_MORT_30_STK")],
na.rm = TRUE)
# Remove old mortality columns
HipKneeTest <- HipKneeTest[, !(names(HipKneeTest) %in% c("Score_MORT_30_AMI",
"Score_MORT_30_COPD",
"Score_MORT_30_HF",
"Score_MORT_30_PN",
"Score_MORT_30_STK"))]Scaling and Normalizing
# Exclude non-numeric columns
non_numeric_columns <- c("State", "FacilityName", "FacilityId", "StateCode")
numeric_columns <- setdiff(names(HipKneeTest), non_numeric_columns)
# Scale and normalize the numeric columns
HipKneeTest_scaled <- as.data.frame(scale(HipKneeTest[, numeric_columns]))
# Combine the scaled data with the non-numeric data
HipKneeTest <- cbind(HipKneeTest_scaled, HipKneeTest[, non_numeric_columns])
# Print the first few rows of the scaled and normalized dataset
head(HipKneeTest)## PredictedReadmissionRate_HIP_KNEE
## 1 -1.1150985
## 2 -0.8611924
## 3 1.0674602
## 4 -0.2314305
## 5 -0.6125646
## 6 -1.3887980
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE EDV HCP_COVID_19
## 1 0.6111897 0.2980455 -0.85464780
## 2 -0.4631912 0.2980455 -0.94878379
## 3 -1.2689769 0.2980455 -1.03246023
## 4 0.6111897 -1.6530882 -3.23942637
## 5 0.3425945 0.2980455 -0.09110027
## 6 -1.0003817 -1.6530882 -1.99473930
## IMM_3 OP_18b OP_29 SAFE_USE_OF_OPIOIDS VTE_1
## 1 0.88316647 0.5118139 -3.8452578 -0.3699263 0.756471728
## 2 0.03799359 -0.8569912 0.3270810 0.8064701 -0.008043768
## 3 -0.69448958 -0.2531066 -0.6095664 0.3359116 -0.008043768
## 4 -1.48331760 -1.1991925 -5.8888523 -0.1346470 -0.335693267
## 5 0.09433845 0.3306485 0.6676801 1.0417494 -1.318641762
## 6 -0.80717930 -0.5953079 0.6676801 -0.3699263 -0.226476767
## Score_COMP_HIP_KNEE Score_PSI_03 Score_PSI_04 Score_PSI_06 Score_PSI_08
## 1 -0.8663258 -0.3816018 0.833989052 -0.3705681 1.226111221
## 2 -1.5883789 0.6794088 0.771436453 -0.5915348 0.006652801
## 3 2.5634263 1.5475084 0.253143493 2.5019979 -1.212805619
## 4 -0.1442727 -0.3237285 -0.001272118 -0.1496015 0.006652801
## 5 0.5777803 -0.8831704 1.075789013 -0.1496015 -1.212805619
## 6 1.1193201 -0.3430196 0.771436453 -0.1496015 0.006652801
## Score_PSI_09 Score_PSI_10 Score_PSI_11 Score_PSI_12 Score_PSI_13 Score_PSI_14
## 1 -0.25064332 -1.16016845 1.5615798 1.1419636 2.67912966 -0.8980388
## 2 0.35503765 -0.54384358 -0.5718768 -0.7209359 -0.80202648 -0.3937431
## 3 5.88692387 -0.83860765 -0.5140421 -0.2803853 -0.88929781 -0.7019238
## 4 -0.04874966 -0.00790891 -0.1670341 0.3867341 -0.09415908 -0.2256446
## 5 -1.28030097 0.39404209 -1.5807704 -0.6831744 0.25492622 -0.4217595
## 6 -0.14969649 -0.16868931 -0.3373251 -0.3307339 -0.39476030 1.2592260
## Score_PSI_15 Payment_PAYM_90_HIP_KNEE Score_Ovr_MORT State
## 1 -0.52773226 0.59439362 -0.4803681 AL
## 2 -0.58885917 -1.55714742 2.0163981 AL
## 3 0.72536925 0.43284798 1.7753310 AL
## 4 -0.06928049 -0.07442593 3.4283624 AL
## 5 0.23635402 0.48635354 0.8455008 AL
## 6 -0.19153430 1.28636461 1.4826067 AL
## FacilityName FacilityId StateCode
## 1 SOUTHEAST HEALTH MEDICAL CENTER 010001 001
## 2 MARSHALL MEDICAL CENTERS 010005 001
## 3 NORTH ALABAMA MEDICAL CENTER 010006 001
## 4 MIZELL MEMORIAL HOSPITAL 010007 001
## 5 ST. VINCENT'S EAST 010011 001
## 6 DEKALB REGIONAL MEDICAL CENTER 010012 001Reassess heatmap with engineered mortality data
# Compute correlation matrix
cor_matrix <- cor(HipKneeTest %>% select_if(is.numeric), use = "pairwise.complete.obs")
# Melt the correlation matrix into a long format
cor_melted <- melt(cor_matrix)
# Plot heatmap
ggplot(cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1), name = "Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Figure 5. Correlation Heatmap of Numeric Variables")
Save the data for future ease of use
save(HipKneeTest, file = "HipKneeTest.RData")Descriptive Statistics (AC)
# Create a summary table of descriptive statistics
descr_stats <- describe(HipKneeTrain)
# Remove the rows with Facility ID, State and State code, and facility name
descr_stats <- descr_stats %>% filter(vars != c(1, 23, 24, 26))## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `vars != c(1, 23, 24, 26)`.
## Caused by warning in `vars != c(1, 23, 24, 26)`:
## ! longer object length is not a multiple of shorter object length# Remove columns 1, 2, 5, and 6
descr_stats <- descr_stats[, -c(1, 2, 5, 6)]
# Create a table with kable
kable(descr_stats, format = "html", caption = "Descriptive Statistics for All Numeric Variables in Final Dataset") %>%
kable_styling(
bootstrap_options = c("hover", "striped", "responsive")
) %>%
column_spec(1, bold = TRUE) %>%
column_spec(2, width = "5em") %>%
row_spec(0, bold = TRUE, background = "#f2f2f2")| mean | sd | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|
| HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE | 0.00000 | 1.00000 | 0.7964386 | -5.566501 | 3.0285467 | 8.595047 | -0.5851721 | 1.5050011 | 0.0233571 |
| EDV | 0.00000 | 1.00000 | 1.4463754 | -1.653088 | 1.2736124 | 2.926700 | -0.1385316 | -1.1578643 | 0.0233571 |
| HCP_COVID_19 | 0.00000 | 1.00000 | 0.8684108 | -5.885694 | 1.1640463 | 7.049740 | -1.3148000 | 2.1219028 | 0.0233571 |
| IMM_3 | 0.00000 | 1.00000 | 0.9189058 | -4.244216 | 1.1648908 | 5.409107 | -1.0958217 | 0.7241943 | 0.0233571 |
| OP_18b | 0.00000 | 1.00000 | 0.8953193 | -2.567998 | 7.9999828 | 10.567980 | 0.9817063 | 3.3080181 | 0.0233571 |
| OP_29 | 0.00000 | 1.00000 | 0.3787292 | -7.847297 | 0.6676801 | 8.514977 | -3.3057142 | 14.4641144 | 0.0233571 |
| SAFE_USE_OF_OPIOIDS | 0.00000 | 1.00000 | 0.6976501 | -3.663836 | 6.9237316 | 10.587568 | 0.5890847 | 3.1474800 | 0.0233571 |
| VTE_1 | 0.00000 | 1.00000 | 0.4857731 | -9.400663 | 0.9749047 | 10.375567 | -4.3773341 | 29.2741519 | 0.0233571 |
| Score_PSI_03 | 0.00000 | 1.00000 | 0.5434188 | -1.037499 | 11.0387303 | 12.076230 | 3.5027779 | 21.8351982 | 0.0233571 |
| Score_PSI_04 | 0.00000 | 1.00000 | 0.8206364 | -4.317401 | 3.8370394 | 8.154441 | -0.0656641 | 1.2542628 | 0.0233571 |
| Score_PSI_06 | 0.00000 | 1.00000 | 0.6552102 | -2.801201 | 5.8164972 | 8.617698 | 0.9085914 | 1.9551905 | 0.0233571 |
| Score_PSI_08 | 0.00000 | 1.00000 | 0.0000000 | -3.651722 | 4.8844865 | 8.536209 | 0.5502254 | 1.7580966 | 0.0233571 |
| Score_PSI_09 | 0.00000 | 1.00000 | 0.6884533 | -2.855072 | 7.2396114 | 10.094683 | 1.2648156 | 4.8211377 | 0.0233571 |
| Score_PSI_10 | 0.00000 | 1.00000 | 0.3972884 | -2.955550 | 7.9775177 | 10.933067 | 1.8287437 | 7.4056616 | 0.0233571 |
| Score_PSI_11 | 0.00000 | 1.00000 | 0.7145473 | -2.004891 | 12.8618308 | 14.866722 | 2.7479837 | 22.3393252 | 0.0233571 |
| Score_PSI_12 | 0.00000 | 1.00000 | 0.8397775 | -2.483138 | 4.9432855 | 7.426424 | 0.9512312 | 1.6446910 | 0.0233571 |
| Score_PSI_13 | 0.00000 | 1.00000 | 0.7188248 | -3.022597 | 5.3360566 | 8.358654 | 1.0438233 | 2.7498825 | 0.0233571 |
| Score_PSI_14 | 0.00000 | 1.00000 | 0.5399830 | -2.635057 | 6.6944124 | 9.329469 | 1.9129489 | 6.4857308 | 0.0233571 |
| Score_PSI_15 | 0.00000 | 1.00000 | 0.6797006 | -2.300412 | 7.1131306 | 9.413543 | 1.7127063 | 5.4101230 | 0.0233571 |
| Score_Ovr_MORT | 0.00000 | 1.00000 | 0.8935151 | -4.216908 | 3.7555248 | 7.972433 | -0.0667113 | 0.5511229 | 0.0233571 |
| State* | 24.69449 | 14.44480 | 20.7564000 | 1.000000 | 50.0000000 | 49.000000 | 0.0222112 | -1.3657016 | 0.3373885 |
| FacilityName* | 902.62630 | 517.85331 | 667.1700000 | 1.000000 | 1796.0000000 | 1795.000000 | -0.0093756 | -1.2029068 | 12.0955473 |
| FacilityId* | 917.00000 | 529.28584 | 679.0308000 | 1.000000 | 1833.0000000 | 1832.000000 | 0.0000000 | -1.2019642 | 12.3625779 |
| StateCode* | 24.72995 | 14.53683 | 20.7564000 | 1.000000 | 50.0000000 | 49.000000 | 0.0160810 | -1.3497989 | 0.3395381 |
# Select numeric columns
numeric_columns <- HipKneeTrain %>% select_if(is.numeric)
# Melt the data for easier plotting with ggplot2
numeric_melted <- melt(numeric_columns)## No id variables; using all as measure variables# Create histograms
ggplot(numeric_melted, aes(x = value)) +
geom_histogram(bins = 30, fill = "blue", color = "black") +
facet_wrap(~variable, scales = "free_x") +
theme_minimal() +
labs(title = "Histograms of Numeric Variables", x = "Value", y = "Frequency")
Segmentation Analysis
k-means clustering (SE)
# Select numeric columns for clustering
numeric_columns <- HipKneeTrain %>% select_if(is.numeric)
# Standardize features
X_scaled <- scale(numeric_columns)
# Determine optimal number of clusters using elbow plot
set.seed(123)
elbow_plot <- fviz_nbclust(X_scaled, kmeans, method = "wss", k.max = 10) +
labs(title = "Elbow Plot for Optimal k")
print(elbow_plot)
# Optimal K = 3
optimal_k <- 3
kmeans_result <- kmeans(X_scaled, centers = optimal_k, nstart = 25)
# Create a new df for K-Means Clustering results
HipKneeTrain_K_Means <- HipKneeTrain %>%
mutate(Cluster = as.factor(kmeans_result$cluster))
# Visualize clusters
fviz_cluster(kmeans_result, data = X_scaled,
ellipse.type = "convex",
palette = "jco",
ggtheme = theme_minimal())
# Cluster characteristics
cluster_summary <- HipKneeTrain_K_Means %>%
group_by(Cluster) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
print(cluster_summary)## # A tibble: 3 × 22
## Cluster PredictedReadmissionRate…¹ HcahpsLinearMeanValu…² EDV HCP_COVID_19
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 -0.212 0.425 -0.0695 0.451
## 2 2 0.372 -0.301 0.520 0.252
## 3 3 0.00972 -0.360 -0.300 -0.818
## # ℹ abbreviated names: ¹PredictedReadmissionRate_HIP_KNEE,
## # ²HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE
## # ℹ 17 more variables: IMM_3 <dbl>, OP_18b <dbl>, OP_29 <dbl>,
## # SAFE_USE_OF_OPIOIDS <dbl>, VTE_1 <dbl>, Score_PSI_03 <dbl>,
## # Score_PSI_04 <dbl>, Score_PSI_06 <dbl>, Score_PSI_08 <dbl>,
## # Score_PSI_09 <dbl>, Score_PSI_10 <dbl>, Score_PSI_11 <dbl>,
## # Score_PSI_12 <dbl>, Score_PSI_13 <dbl>, Score_PSI_14 <dbl>, …# Visualize feature distributions across clusters
features_to_plot <- c("PredictedReadmissionRate_HIP_KNEE", "HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE", "SAFE_USE_OF_OPIOIDS")
for (feature in features_to_plot) {
p <- ggplot(HipKneeTrain_K_Means, aes(x = Cluster, y = .data[[feature]], fill = Cluster)) +
geom_boxplot() +
theme_minimal() +
labs(title = paste("Distribution of", feature, "across clusters"))
print(p)
}
Hierarchical Clustering (SE)
# Perform PCA
pca_result <- prcomp(X_scaled, center = TRUE, scale. = TRUE)
# Visualize variance
fviz_eig(pca_result, addlabels = TRUE)
# Factor map
fviz_pca_var(pca_result, col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
# PC scores, three components
pca_scores <- as.data.frame(pca_result$x[, 1:3])
# Store PCA results in a new dataframe
HipKneeTrain_PCA <- HipKneeTrain %>%
select_if(is.numeric) %>%
bind_cols(pca_scores)
# Hierarchical Clustering
# Compute distance matrix
dist_matrix <- dist(X_scaled, method = "euclidean")
# Perform hierarchical clustering
hc_result <- hclust(dist_matrix, method = "ward.D2")
# Compute WCSS for different number of clusters
wcss <- sapply(1:10, function(k) {
clusters <- cutree(hc_result, k)
cluster_data <- scale(X_scaled)
tot.withinss <- sum(sapply(unique(clusters), function(c) {
sum(dist(cluster_data[clusters == c, , drop = FALSE])^2)
}))
return(tot.withinss)
})
# Plot WCSS
plot(1:10, wcss, type = "b", xlab = "Number of Clusters", ylab = "WCSS")
# Create clusters with optimal number of clusters from WCSS plot
k <- 3
hc_clusters <- cutree(hc_result, k = k)
# Store hierarchical clustering results in a new dataframe
HipKneeTrain_HC <- HipKneeTrain %>%
mutate(HC_Cluster = as.factor(hc_clusters))
# Visualize clusters using first three PCs
pca_plot_data <- cbind(pca_scores[, 1:3], Cluster = hc_clusters)
fviz_cluster(list(data = pca_plot_data, cluster = hc_clusters),
ellipse.type = "convex",
palette = "jco",
ggtheme = theme_minimal(),
main = "Hierarchical Clustering Results (PCA)")
# Analyze cluster characteristics
hc_cluster_summary <- HipKneeTrain_HC %>%
group_by(HC_Cluster) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
print(hc_cluster_summary)## # A tibble: 3 × 22
## HC_Cluster PredictedReadmissionR…¹ HcahpsLinearMeanValu…² EDV HCP_COVID_19
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.213 -0.336 -0.0969 -1.16
## 2 2 -0.630 0.540 -0.708 0.0555
## 3 3 0.203 -0.126 0.334 0.342
## # ℹ abbreviated names: ¹PredictedReadmissionRate_HIP_KNEE,
## # ²HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE
## # ℹ 17 more variables: IMM_3 <dbl>, OP_18b <dbl>, OP_29 <dbl>,
## # SAFE_USE_OF_OPIOIDS <dbl>, VTE_1 <dbl>, Score_PSI_03 <dbl>,
## # Score_PSI_04 <dbl>, Score_PSI_06 <dbl>, Score_PSI_08 <dbl>,
## # Score_PSI_09 <dbl>, Score_PSI_10 <dbl>, Score_PSI_11 <dbl>,
## # Score_PSI_12 <dbl>, Score_PSI_13 <dbl>, Score_PSI_14 <dbl>, …# Visualize feature distributions across clusters
features_to_plot <- c("PredictedReadmissionRate_HIP_KNEE", "HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE", "SAFE_USE_OF_OPIOIDS")
for (feature in features_to_plot) {
p <- ggplot(HipKneeTrain_HC, aes_string(x = "HC_Cluster", y = feature, fill = "HC_Cluster")) +
geom_boxplot() +
theme_minimal() +
labs(title = paste("Distribution of", feature, "across Hierarchical Clusters"))
print(p)
}## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
> This is a preliminary segmentation analysis and we would go forth
and tighten/tidy this up some more. However, just initial impression,
I’m not sure clustering is entirely beneficial with our dataset. What
are your thoughts on our preliminary segmentation analysis? Any ideas in
which we could improve our clustering to perhaps be more meaningful?
Supervised Modeling with Quantitative Variable
Random Forest (SE)
Creating the model
# Remove unwanted columns from the dataset
HipKneeTrain_RF <- HipKneeTrain %>%
select(-State, -FacilityName, -FacilityId)
# Define mtry parameter grid
grid <- expand.grid(
mtry = c(2, 4, 6, 8)
)
# Define CV
train_control <- trainControl(
method = "cv",
number = 7,
verboseIter = TRUE
)
# Train the Random Forest model with grid search
rf_grid_search <- train(
PredictedReadmissionRate_HIP_KNEE ~ .,
data = HipKneeTrain_RF,
method = "rf",
trControl = train_control,
tuneGrid = grid,
importance = TRUE,
ntree = 100
)## + Fold1: mtry=2
## - Fold1: mtry=2
## + Fold1: mtry=4
## - Fold1: mtry=4
## + Fold1: mtry=6
## - Fold1: mtry=6
## + Fold1: mtry=8
## - Fold1: mtry=8
## + Fold2: mtry=2
## - Fold2: mtry=2
## + Fold2: mtry=4
## - Fold2: mtry=4
## + Fold2: mtry=6
## - Fold2: mtry=6
## + Fold2: mtry=8
## - Fold2: mtry=8
## + Fold3: mtry=2
## - Fold3: mtry=2
## + Fold3: mtry=4
## - Fold3: mtry=4
## + Fold3: mtry=6
## - Fold3: mtry=6
## + Fold3: mtry=8
## - Fold3: mtry=8
## + Fold4: mtry=2
## - Fold4: mtry=2
## + Fold4: mtry=4
## - Fold4: mtry=4
## + Fold4: mtry=6
## - Fold4: mtry=6
## + Fold4: mtry=8
## - Fold4: mtry=8
## + Fold5: mtry=2
## - Fold5: mtry=2
## + Fold5: mtry=4
## - Fold5: mtry=4
## + Fold5: mtry=6
## - Fold5: mtry=6
## + Fold5: mtry=8
## - Fold5: mtry=8
## + Fold6: mtry=2
## - Fold6: mtry=2
## + Fold6: mtry=4
## - Fold6: mtry=4
## + Fold6: mtry=6
## - Fold6: mtry=6
## + Fold6: mtry=8
## - Fold6: mtry=8
## + Fold7: mtry=2
## - Fold7: mtry=2
## + Fold7: mtry=4
## - Fold7: mtry=4
## + Fold7: mtry=6
## - Fold7: mtry=6
## + Fold7: mtry=8
## - Fold7: mtry=8
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 4 on full training set# Best parameters
best_params <- rf_grid_search$bestTune
print(best_params)## mtry
## 2 4# Extract feature importances
best_rf_model <- rf_grid_search$finalModel
feature_importances <- importance(best_rf_model)
# Convert feature importances to a df
feature_importances_df <- as.data.frame(feature_importances)
feature_importances_df$Feature <- rownames(feature_importances_df)
# Sort importances by %IncMSE
sorted_by_inc_mse <- feature_importances_df %>%
arrange(desc(`%IncMSE`))
# Sort importances by IncNodePurity
sorted_by_inc_node_purity <- feature_importances_df %>%
arrange(desc(IncNodePurity))
# Print importances
cat("Feature Importances by %IncMSE:\n")## Feature Importances by %IncMSE:print(sorted_by_inc_mse)## %IncMSE IncNodePurity
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE 6.77636522 81.8353680
## Score_PSI_11 5.31121404 69.1632350
## OP_18b 5.02841477 71.5094862
## Score_PSI_03 4.98064201 65.3264581
## VTE_1 4.59400482 50.8666195
## Score_PSI_15 4.47932042 66.9283663
## StateCode017 4.36976816 8.1445452
## Score_PSI_06 3.97762334 47.1318302
## Score_PSI_13 3.92665567 71.3468378
## StateCode009 3.88332727 15.3537820
## Score_Ovr_MORT 3.87731156 65.6204724
## EDV 3.68066032 35.9307100
## Score_PSI_04 3.65024483 60.0732712
## Score_PSI_09 3.12785115 57.6711232
## Score_PSI_14 2.88431986 62.4934519
## Score_PSI_12 2.81478275 67.0271357
## Score_PSI_10 2.52535674 67.4720637
## StateCode032 2.48286394 4.4324120
## StateCode046 2.38404823 3.7623217
## StateCode025 2.14222804 4.8612472
## StateCode041 2.08192748 3.6160080
## StateCode050 1.92622910 0.6199415
## StateCode022 1.90106818 7.8927146
## Score_PSI_08 1.87470786 23.8388425
## StateCode018 1.70920672 3.2280526
## StateCode012 1.69686232 0.5890989
## HCP_COVID_19 1.62760082 58.4939642
## SAFE_USE_OF_OPIOIDS 1.61579377 54.1806881
## StateCode029 1.18873667 0.6262575
## IMM_3 1.15974441 48.0560865
## StateCode024 1.04222682 1.4031140
## StateCode034 0.98438207 0.3330192
## StateCode005 0.93326628 6.7359302
## StateCode037 0.92286453 2.0923683
## StateCode004 0.88363996 2.9154904
## StateCode002 0.78267680 2.4971894
## StateCode014 0.68283870 6.3734678
## StateCode044 0.67365766 2.2648032
## StateCode006 0.47980065 2.8907199
## StateCode047 0.47800300 2.7877602
## OP_29 0.44940719 42.5091422
## StateCode016 0.44293099 6.1455596
## StateCode010 0.37219081 3.0495874
## StateCode015 0.36718178 1.6023607
## StateCode035 0.34684329 8.0869780
## StateCode048 0.21340381 1.4172689
## StateCode023 0.07145179 3.9464995
## StateCode028 -0.04459152 1.2654268
## StateCode019 -0.23136632 2.1028641
## StateCode043 -0.26678919 6.8041501
## StateCode045 -0.42644646 0.6519616
## StateCode031 -0.45041737 0.8580269
## StateCode036 -0.54235575 3.4190281
## StateCode042 -0.54563929 2.5450266
## StateCode038 -0.65510522 5.1636552
## StateCode008 -0.65978290 0.1244015
## StateCode030 -0.67665223 7.1509433
## StateCode039 -0.69039143 0.5890982
## StateCode021 -0.71982035 2.1891517
## StateCode003 -0.79370327 1.8538204
## StateCode020 -0.83256151 2.3613580
## StateCode033 -0.84543023 3.0725125
## StateCode007 -0.86028939 2.3974591
## StateCode040 -1.12267289 4.9403470
## StateCode011 -1.24351584 0.7783814
## StateCode049 -1.51615304 3.8998805
## StateCode027 -1.73425048 4.5546366
## StateCode013 -1.75186096 5.6678722
## StateCode026 -2.07424045 1.2077184
## Feature
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE
## Score_PSI_11 Score_PSI_11
## OP_18b OP_18b
## Score_PSI_03 Score_PSI_03
## VTE_1 VTE_1
## Score_PSI_15 Score_PSI_15
## StateCode017 StateCode017
## Score_PSI_06 Score_PSI_06
## Score_PSI_13 Score_PSI_13
## StateCode009 StateCode009
## Score_Ovr_MORT Score_Ovr_MORT
## EDV EDV
## Score_PSI_04 Score_PSI_04
## Score_PSI_09 Score_PSI_09
## Score_PSI_14 Score_PSI_14
## Score_PSI_12 Score_PSI_12
## Score_PSI_10 Score_PSI_10
## StateCode032 StateCode032
## StateCode046 StateCode046
## StateCode025 StateCode025
## StateCode041 StateCode041
## StateCode050 StateCode050
## StateCode022 StateCode022
## Score_PSI_08 Score_PSI_08
## StateCode018 StateCode018
## StateCode012 StateCode012
## HCP_COVID_19 HCP_COVID_19
## SAFE_USE_OF_OPIOIDS SAFE_USE_OF_OPIOIDS
## StateCode029 StateCode029
## IMM_3 IMM_3
## StateCode024 StateCode024
## StateCode034 StateCode034
## StateCode005 StateCode005
## StateCode037 StateCode037
## StateCode004 StateCode004
## StateCode002 StateCode002
## StateCode014 StateCode014
## StateCode044 StateCode044
## StateCode006 StateCode006
## StateCode047 StateCode047
## OP_29 OP_29
## StateCode016 StateCode016
## StateCode010 StateCode010
## StateCode015 StateCode015
## StateCode035 StateCode035
## StateCode048 StateCode048
## StateCode023 StateCode023
## StateCode028 StateCode028
## StateCode019 StateCode019
## StateCode043 StateCode043
## StateCode045 StateCode045
## StateCode031 StateCode031
## StateCode036 StateCode036
## StateCode042 StateCode042
## StateCode038 StateCode038
## StateCode008 StateCode008
## StateCode030 StateCode030
## StateCode039 StateCode039
## StateCode021 StateCode021
## StateCode003 StateCode003
## StateCode020 StateCode020
## StateCode033 StateCode033
## StateCode007 StateCode007
## StateCode040 StateCode040
## StateCode011 StateCode011
## StateCode049 StateCode049
## StateCode027 StateCode027
## StateCode013 StateCode013
## StateCode026 StateCode026cat("\nFeature Importances by IncNodePurity:\n")##
## Feature Importances by IncNodePurity:print(sorted_by_inc_node_purity)## %IncMSE IncNodePurity
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE 6.77636522 81.8353680
## OP_18b 5.02841477 71.5094862
## Score_PSI_13 3.92665567 71.3468378
## Score_PSI_11 5.31121404 69.1632350
## Score_PSI_10 2.52535674 67.4720637
## Score_PSI_12 2.81478275 67.0271357
## Score_PSI_15 4.47932042 66.9283663
## Score_Ovr_MORT 3.87731156 65.6204724
## Score_PSI_03 4.98064201 65.3264581
## Score_PSI_14 2.88431986 62.4934519
## Score_PSI_04 3.65024483 60.0732712
## HCP_COVID_19 1.62760082 58.4939642
## Score_PSI_09 3.12785115 57.6711232
## SAFE_USE_OF_OPIOIDS 1.61579377 54.1806881
## VTE_1 4.59400482 50.8666195
## IMM_3 1.15974441 48.0560865
## Score_PSI_06 3.97762334 47.1318302
## OP_29 0.44940719 42.5091422
## EDV 3.68066032 35.9307100
## Score_PSI_08 1.87470786 23.8388425
## StateCode009 3.88332727 15.3537820
## StateCode017 4.36976816 8.1445452
## StateCode035 0.34684329 8.0869780
## StateCode022 1.90106818 7.8927146
## StateCode030 -0.67665223 7.1509433
## StateCode043 -0.26678919 6.8041501
## StateCode005 0.93326628 6.7359302
## StateCode014 0.68283870 6.3734678
## StateCode016 0.44293099 6.1455596
## StateCode013 -1.75186096 5.6678722
## StateCode038 -0.65510522 5.1636552
## StateCode040 -1.12267289 4.9403470
## StateCode025 2.14222804 4.8612472
## StateCode027 -1.73425048 4.5546366
## StateCode032 2.48286394 4.4324120
## StateCode023 0.07145179 3.9464995
## StateCode049 -1.51615304 3.8998805
## StateCode046 2.38404823 3.7623217
## StateCode041 2.08192748 3.6160080
## StateCode036 -0.54235575 3.4190281
## StateCode018 1.70920672 3.2280526
## StateCode033 -0.84543023 3.0725125
## StateCode010 0.37219081 3.0495874
## StateCode004 0.88363996 2.9154904
## StateCode006 0.47980065 2.8907199
## StateCode047 0.47800300 2.7877602
## StateCode042 -0.54563929 2.5450266
## StateCode002 0.78267680 2.4971894
## StateCode007 -0.86028939 2.3974591
## StateCode020 -0.83256151 2.3613580
## StateCode044 0.67365766 2.2648032
## StateCode021 -0.71982035 2.1891517
## StateCode019 -0.23136632 2.1028641
## StateCode037 0.92286453 2.0923683
## StateCode003 -0.79370327 1.8538204
## StateCode015 0.36718178 1.6023607
## StateCode048 0.21340381 1.4172689
## StateCode024 1.04222682 1.4031140
## StateCode028 -0.04459152 1.2654268
## StateCode026 -2.07424045 1.2077184
## StateCode031 -0.45041737 0.8580269
## StateCode011 -1.24351584 0.7783814
## StateCode045 -0.42644646 0.6519616
## StateCode029 1.18873667 0.6262575
## StateCode050 1.92622910 0.6199415
## StateCode012 1.69686232 0.5890989
## StateCode039 -0.69039143 0.5890982
## StateCode034 0.98438207 0.3330192
## StateCode008 -0.65978290 0.1244015
## Feature
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE
## OP_18b OP_18b
## Score_PSI_13 Score_PSI_13
## Score_PSI_11 Score_PSI_11
## Score_PSI_10 Score_PSI_10
## Score_PSI_12 Score_PSI_12
## Score_PSI_15 Score_PSI_15
## Score_Ovr_MORT Score_Ovr_MORT
## Score_PSI_03 Score_PSI_03
## Score_PSI_14 Score_PSI_14
## Score_PSI_04 Score_PSI_04
## HCP_COVID_19 HCP_COVID_19
## Score_PSI_09 Score_PSI_09
## SAFE_USE_OF_OPIOIDS SAFE_USE_OF_OPIOIDS
## VTE_1 VTE_1
## IMM_3 IMM_3
## Score_PSI_06 Score_PSI_06
## OP_29 OP_29
## EDV EDV
## Score_PSI_08 Score_PSI_08
## StateCode009 StateCode009
## StateCode017 StateCode017
## StateCode035 StateCode035
## StateCode022 StateCode022
## StateCode030 StateCode030
## StateCode043 StateCode043
## StateCode005 StateCode005
## StateCode014 StateCode014
## StateCode016 StateCode016
## StateCode013 StateCode013
## StateCode038 StateCode038
## StateCode040 StateCode040
## StateCode025 StateCode025
## StateCode027 StateCode027
## StateCode032 StateCode032
## StateCode023 StateCode023
## StateCode049 StateCode049
## StateCode046 StateCode046
## StateCode041 StateCode041
## StateCode036 StateCode036
## StateCode018 StateCode018
## StateCode033 StateCode033
## StateCode010 StateCode010
## StateCode004 StateCode004
## StateCode006 StateCode006
## StateCode047 StateCode047
## StateCode042 StateCode042
## StateCode002 StateCode002
## StateCode007 StateCode007
## StateCode020 StateCode020
## StateCode044 StateCode044
## StateCode021 StateCode021
## StateCode019 StateCode019
## StateCode037 StateCode037
## StateCode003 StateCode003
## StateCode015 StateCode015
## StateCode048 StateCode048
## StateCode024 StateCode024
## StateCode028 StateCode028
## StateCode026 StateCode026
## StateCode031 StateCode031
## StateCode011 StateCode011
## StateCode045 StateCode045
## StateCode029 StateCode029
## StateCode050 StateCode050
## StateCode012 StateCode012
## StateCode039 StateCode039
## StateCode034 StateCode034
## StateCode008 StateCode008Assessing Random Forest Performance
# Remove columns from the test set to match train set
HipKneeTest_RF <- HipKneeTest %>%
select(-State, -FacilityName, -FacilityId)
# Make predictions on test set
rf_predictions <- predict(rf_grid_search, newdata = HipKneeTest_RF)
# Actual values
actual_values <- HipKneeTest$PredictedReadmissionRate_HIP_KNEE
# Calculate RMSE
mse <- mean((rf_predictions - actual_values)^2)
rmse <- sqrt(mse)
# Calculate R-squared
ss_total <- sum((actual_values - mean(actual_values))^2)
ss_residual <- sum((rf_predictions - actual_values)^2)
r_squared <- 1 - (ss_residual / ss_total)
# Print RMSE and R-squared
cat("RMSE on test set:\n")## RMSE on test set:print(rmse)## [1] 0.5452123cat("\nR-squared on test set:\n")##
## R-squared on test set:print(r_squared)## [1] 0.7025813Testing assumptions
# Calculate residuals
residuals_rf <- actual_values - rf_predictions
# Residuals vs Fitted Values plot
ggplot(data = NULL, aes(x = rf_predictions, y = residuals_rf)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "loess", se = FALSE, color = "blue") +
labs(title = "Residuals vs Fitted Values",
x = "Fitted Values",
y = "Residuals") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
# Histogram of residuals
ggplot(data = NULL, aes(x = residuals_rf)) +
geom_histogram(binwidth = 0.1, fill = "blue", alpha = 0.7, boundary = 0) +
labs(title = "Histogram of Residuals",
x = "Residuals",
y = "Frequency") +
theme_minimal()
# QQ plot of residuals
qqnorm(residuals_rf, main = "QQ Plot of Residuals")
qqline(residuals_rf, col = "red")
# Perform Durbin-Watson test for autocorrelation in residuals
dw_test_result <- dwtest(lm(residuals_rf ~ rf_predictions))
print(dw_test_result)##
## Durbin-Watson test
##
## data: lm(residuals_rf ~ rf_predictions)
## DW = 1.7312, p-value = 4.151e-09
## alternative hypothesis: true autocorrelation is greater than 0Elastic Net (AC)
Creating the model
# Separate predictors and response variable in the training set
x_train <- as.matrix(HipKneeTrain %>% select(-c(State, FacilityName, PredictedReadmissionRate_HIP_KNEE)))
y_train <- HipKneeTrain$PredictedReadmissionRate_HIP_KNEE
# Separate predictors and response variable in the test set
x_test <- as.matrix(HipKneeTest %>% select(-c(State, FacilityName, PredictedReadmissionRate_HIP_KNEE)))
y_test <- HipKneeTest$PredictedReadmissionRate_HIP_KNEE
# Define the grid of hyperparameters
searchGrid <- expand.grid(.alpha = seq(0, 1, length.out = 10),
.lambda = seq(0, 5, length.out = 15))
# Define the train control
ctrl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 5,
search = "grid",
verboseIter = FALSE)
# Set up cross-validation
elasticnet_model <- train(
x = x_train,
y = y_train,
method = "glmnet",
trControl = ctrl,
tuneGrid = searchGrid
)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.# Best hyperparameters
best_alpha <- elasticnet_model$bestTune$alpha
best_lambda <- elasticnet_model$bestTune$lambda
# Print best alpha and lambda
print(paste("Best Alpha: ", best_alpha))## [1] "Best Alpha: 0"print(paste("Best Lambda: ", best_lambda))## [1] "Best Lambda: 0"Assessing performance
# Make predictions on the test set
predictions <- predict(elasticnet_model, newdata = x_test)
# Calculate RMSE
rmse <- sqrt(mean((predictions - y_test)^2))
# Print RMSE
print(paste("RMSE on Test Set: ", rmse))## [1] "RMSE on Test Set: 0.937464431413324"# Calculate performance metrics on the test set
performance <- postResample(pred = predictions, obs = y_test)
# Extract and print R-squared
r_squared <- performance["Rsquared"]
print(paste("R^2 on Test Set: ", r_squared))## [1] "R^2 on Test Set: 0.120720477945213"# Get the feature importance
important <- varImp(elasticnet_model)$importance
# View the feature importance
important %>%
mutate(Feature = rownames(important)) %>%
mutate(Feature = gsub("\\.", " ", Feature)) %>%
arrange(desc(Overall)) %>%
ggplot(aes(y = Overall, fill = Overall, x = fct_reorder(Feature, Overall))) +
geom_col() +
scale_fill_continuous(low = "lightblue", high = "darkblue") +
coord_flip() +
theme_minimal() +
labs(title = "Feature importance as determined by Elastic Net",
x = "",
y = "Importance",
fill = "")
SVM (AC)
Creating the model
Selecting the kernel
# Convert x_train and x_test back to data frames
x_train <- as.data.frame(x_train)
x_test <- as.data.frame(x_test)
# Ensure all columns are numeric
x_train[] <- lapply(x_train, as.numeric)
x_test[] <- lapply(x_test, as.numeric)
# Convert y_train to a data frame
train_data <- cbind(y_train = y_train, x_train)
# Define the grid for kernel types
searchGrid_kernel <- expand.grid(
.kernel = c("linear", "polynomial", "radial", "sigmoid")
)
# Train the SVM model with kernel tuning
svm_tune_kernel <- tune(svm,
y_train ~ .,
data = train_data,
ranges = searchGrid_kernel,
tunecontrol = tune.control(
sampling = "cross",
cross = 10
)
)
# Extract the best kernel
best_kernel <- svm_tune_kernel$best.model$kernel
if (best_kernel == 0) {
kernel_description <- "Linear kernel"
} else if (best_kernel == 1) {
kernel_description <- "Polynomial kernel"
} else if (best_kernel == 2) {
kernel_description <- "Radial kernel"
} else if (best_kernel == 3) {
kernel_description <- "Sigmoid kernel"
} else {
kernel_description <- "Unknown kernel"
}
cat("Best Kernel Description:", kernel_description, "\n")## Best Kernel Description: Radial kernelTuning gamma and cost
# Define the grid for gamma
searchGrid_gamma <- expand.grid(
gamma = c(0.01, 0.1, 1)
)
# Train the SVM model with gamma tuning
svm_tune_gamma <- tune(svm,
y_train ~ .,
data = train_data,
ranges = searchGrid_gamma,
kernel = "radial",
tunecontrol = tune.control(
sampling = "cross",
cross = 10
)
)
# Extract the best gamma
best_gamma <- svm_tune_gamma$best.model$gamma
cat("Best Gamma:", best_gamma, "\n")## Best Gamma: 0.01# Define the grid for cost
searchGrid_cost <- expand.grid(
C = c(0.1, 1, 10)
)
# Train the SVM model with cost tuning
svm_tune_cost <- tune(svm,
y_train ~ .,
data = train_data,
ranges = searchGrid_cost,
kernel = "radial",
tunecontrol = tune.control(
sampling = "cross",
cross = 10
)
)
# Extract the best cost
best_cost <- svm_tune_cost$best.model$cost
cat("Best Cost:", best_cost, "\n")## Best Cost: 1Create the final model
# Final model with best parameters
svm_final <- svm(y_train ~ .,
data = train_data,
kernel = "radial",
C = 1,
gamma = 0.01,
probability = TRUE)Assessing performance
# Make predictions on the test set
predictions <- predict(svm_final, x_test, type = "response")
# Calculate RMSE
rmse <- sqrt(mean((predictions - y_test)^2))
cat("RMSE on Test Set:", rmse, "\n")## RMSE on Test Set: 0.8959786# Calculate R-squared
rss <- sum((y_test - predictions)^2)
tss <- sum((y_test - mean(y_test))^2)
r_squared <- 1 - (rss / tss)
cat("R-squared on Test Set:", r_squared, "\n")## R-squared on Test Set: 0.1967841Supervised Modeling with Qualitative Variable
Categorizing the target variable (AC)
# Check for median
print(median(HipKneeTrain$PredictedReadmissionRate_HIP_KNEE, na.rm = TRUE))## [1] -0.07660172print(median(HipKneeTest$PredictedReadmissionRate_HIP_KNEE, na.rm = TRUE))## [1] -0.07660172# Calculate the median of the target variable from the training data
median_value <- median(HipKneeTrain$PredictedReadmissionRate_HIP_KNEE, na.rm = TRUE)
# Categorize the target variable in the training data
HipKneeTrain_Qual <- HipKneeTrain %>%
mutate(TargetCategory = ifelse(PredictedReadmissionRate_HIP_KNEE > median_value, 1, 0))
# Categorize the target variable in the testing data using the median from the training data
HipKneeTest_Qual <- HipKneeTest %>%
mutate(TargetCategory = ifelse(PredictedReadmissionRate_HIP_KNEE > median_value, 1, 0))Random Forest (SE)
Create the model
# Remove unwanted columns from the dataset
HipKneeTrain_QualRF <- HipKneeTrain_Qual %>%
select(-State, -StateCode, -FacilityName, -FacilityId, -PredictedReadmissionRate_HIP_KNEE)
# Repeated CV
train_control <- trainControl(
method = "repeatedcv",
number = 7,
repeats = 3,
verboseIter = TRUE
)
# Ensure the target variable is a factor
HipKneeTrain_QualRF$TargetCategory <- as.factor(HipKneeTrain_QualRF$TargetCategory)
# Optimize mtry with a fixed ntree value
grid_mtry <- expand.grid(
mtry = c(2, 4, 6, 8)
)
# Train the Random Forest model with grid search for mtry
rf_grid_search_mtry <- train(
TargetCategory ~ .,
data = HipKneeTrain_QualRF,
method = "rf",
trControl = train_control,
tuneGrid = grid_mtry,
importance = TRUE,
ntree = 10
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## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 2 on full training set# Print best mtry parameters
best_mtry <- rf_grid_search_mtry$bestTune
print("Best mtry parameters:")## [1] "Best mtry parameters:"print(best_mtry)## mtry
## 1 2# Optimize ntree using the best mtry
grid_ntree <- c(5, 10, 12)
# Define the grid for mtry with the best mtry value
best_mtry_grid <- expand.grid(mtry = best_mtry$mtry)
# Train the Random Forest model with grid search for ntree
rf_results <- lapply(grid_ntree, function(ntree_val) {
train(
TargetCategory ~ .,
data = HipKneeTrain_QualRF,
method = "rf",
trControl = train_control,
tuneGrid = best_mtry_grid,
importance = TRUE,
ntree = ntree_val
)
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## Aggregating results
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set# Find the best ntree based on performance metrics
best_ntree_index <- which.max(sapply(rf_results, function(model) max(model$results$Accuracy)))
best_rf_model_qual <- rf_results[[best_ntree_index]]
best_ntree <- grid_ntree[best_ntree_index]
# Print the best ntree value
print("Best ntree:")## [1] "Best ntree:"print(best_ntree)## [1] 12# Extract feature importances
feature_importances_qual <- importance(best_rf_model_qual$finalModel)
# Convert feature importances to a dataframe
feature_importances_df_qual <- as.data.frame(feature_importances_qual)
feature_importances_df_qual$Feature <- rownames(feature_importances_df_qual)
# Rename columns for clarity
colnames(feature_importances_df_qual) <- c("MeanDecreaseAccuracy", "MeanDecreaseGini", "Feature")
# Sort by MeanDecreaseAccuracy
sorted_by_importance_qual <- feature_importances_df_qual[order(-feature_importances_df_qual$MeanDecreaseAccuracy), ]
# Print feature importances
print("Feature importances:")## [1] "Feature importances:"print(sorted_by_importance_qual)## MeanDecreaseAccuracy
## Score_PSI_08 4.56058445
## Score_PSI_11 3.23911109
## Score_PSI_15 2.69986255
## OP_18b 2.53478448
## SAFE_USE_OF_OPIOIDS 2.27457723
## Score_PSI_13 2.05223180
## Score_PSI_03 1.90522135
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE 1.61777192
## Score_PSI_14 1.38867326
## Score_PSI_10 0.97295813
## Score_PSI_06 0.95639948
## Score_Ovr_MORT 0.71328869
## Score_PSI_12 0.06874967
## Score_PSI_04 -0.19712493
## VTE_1 -0.33225482
## HCP_COVID_19 -0.44727655
## IMM_3 -0.65227437
## EDV -0.84400399
## OP_29 -0.91361041
## Score_PSI_09 -1.70214593
## MeanDecreaseGini Feature
## Score_PSI_08 -0.5393486 2.1512369
## Score_PSI_11 -0.4094027 2.4671986
## Score_PSI_15 -0.5521609 1.6862698
## OP_18b -1.1850598 1.3357500
## SAFE_USE_OF_OPIOIDS 3.1776723 3.8816460
## Score_PSI_13 0.1862987 1.7600335
## Score_PSI_03 -0.1790361 1.4620032
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE 1.9421799 2.6070931
## Score_PSI_14 0.3498097 1.1611913
## Score_PSI_10 -2.0678868 -0.8874782
## Score_PSI_06 0.7416028 1.2782559
## Score_Ovr_MORT 1.5178660 1.4737474
## Score_PSI_12 0.8584101 0.5494682
## Score_PSI_04 -0.1266909 -0.3256979
## VTE_1 2.4209506 1.1983672
## HCP_COVID_19 1.2139174 0.8280830
## IMM_3 2.7060836 1.1546283
## EDV 1.6593191 -0.1002833
## OP_29 1.3044329 0.4518724
## Score_PSI_09 0.6341052 -0.7531748
## NA
## Score_PSI_08 15.68614
## Score_PSI_11 55.23709
## Score_PSI_15 45.24827
## OP_18b 50.41317
## SAFE_USE_OF_OPIOIDS 45.39561
## Score_PSI_13 53.90385
## Score_PSI_03 52.23088
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE 50.22446
## Score_PSI_14 48.94545
## Score_PSI_10 44.53258
## Score_PSI_06 33.60660
## Score_Ovr_MORT 58.76201
## Score_PSI_12 56.46369
## Score_PSI_04 52.38182
## VTE_1 38.73597
## HCP_COVID_19 52.21491
## IMM_3 50.04164
## EDV 18.81958
## OP_29 39.12113
## Score_PSI_09 53.78535
## NA
## Score_PSI_08 Score_PSI_08
## Score_PSI_11 Score_PSI_11
## Score_PSI_15 Score_PSI_15
## OP_18b OP_18b
## SAFE_USE_OF_OPIOIDS SAFE_USE_OF_OPIOIDS
## Score_PSI_13 Score_PSI_13
## Score_PSI_03 Score_PSI_03
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE
## Score_PSI_14 Score_PSI_14
## Score_PSI_10 Score_PSI_10
## Score_PSI_06 Score_PSI_06
## Score_Ovr_MORT Score_Ovr_MORT
## Score_PSI_12 Score_PSI_12
## Score_PSI_04 Score_PSI_04
## VTE_1 VTE_1
## HCP_COVID_19 HCP_COVID_19
## IMM_3 IMM_3
## EDV EDV
## OP_29 OP_29
## Score_PSI_09 Score_PSI_09Assessing the performance
# Remove columns from the test set to match train set
HipKneeTest_QualRF <- HipKneeTest_Qual %>%
select(-State, -StateCode, -FacilityName, -FacilityId, -PredictedReadmissionRate_HIP_KNEE)
# Ensure the target variable is a factor
HipKneeTest_QualRF$TargetCategory <- as.factor(HipKneeTest_QualRF$TargetCategory)
# Predict on the test set using the best model
pred_rf_qual <- predict(best_rf_model_qual, newdata = HipKneeTest_QualRF)
# Calculate accuracy
accuracy_rf_qual <- mean(pred_rf_qual == HipKneeTest_QualRF$TargetCategory)
cat("Accuracy of the Random Forest Model:", accuracy_rf_qual, "\n")## Accuracy of the Random Forest Model: 0.9918167# Calculate the confusion matrix
conf_matrix_rf_qual <- confusionMatrix(pred_rf_qual, HipKneeTest_QualRF$TargetCategory)
# Print the confusion matrix
print(conf_matrix_rf_qual)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 908 6
## 1 9 910
##
## Accuracy : 0.9918
## 95% CI : (0.9865, 0.9954)
## No Information Rate : 0.5003
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9836
##
## Mcnemar's Test P-Value : 0.6056
##
## Sensitivity : 0.9902
## Specificity : 0.9934
## Pos Pred Value : 0.9934
## Neg Pred Value : 0.9902
## Prevalence : 0.5003
## Detection Rate : 0.4954
## Detection Prevalence : 0.4986
## Balanced Accuracy : 0.9918
##
## 'Positive' Class : 0
## # Predict on the test set for probability scores
pred_prob_rf_qual <- predict(best_rf_model_qual, newdata = HipKneeTest_QualRF, type = "prob")
# Extract the predicted probabilities for the positive class
pred_prob_pos <- pred_prob_rf_qual[, "1"]
# Generate predictions object for ROCR
pred <- prediction(pred_prob_pos, HipKneeTest_QualRF$TargetCategory)
# Calculate and plot ROC curve
roc_curve <- performance(pred, "tpr", "fpr")
plot(roc_curve, main = "ROC Curve for Random Forest Model", col = "blue")
# Calculate AUC
auc <- performance(pred, "auc")@y.values[[1]]
print(paste("AUC:", auc))## [1] "AUC: 0.999733919702085"Logistic Regression (AC)
Creating the model
# Ensure factors have consistent levels between training and test sets
factor_columns <- sapply(HipKneeTrain_Qual, is.factor)
HipKneeTest_Qual <- HipKneeTest_Qual %>%
mutate(across(where(is.factor), ~ factor(.x, levels = levels(HipKneeTrain_Qual[[cur_column()]]))))
# Separate predictors and response variable in the training set - removed StateCode to reduce overfitting and improve interpretability
x_train_qual <- HipKneeTrain_Qual %>% select(-c(State, StateCode, FacilityName, FacilityId, PredictedReadmissionRate_HIP_KNEE, TargetCategory))
y_train_qual <- HipKneeTrain_Qual$TargetCategory
# Ensure the response variable is a factor
y_train_qual <- as.factor(y_train_qual)
# Separate predictors and response variable in the test set
x_test_qual <- HipKneeTest_Qual %>% select(-c(State, StateCode, FacilityName, FacilityId, PredictedReadmissionRate_HIP_KNEE, TargetCategory))
y_test_qual <- HipKneeTest_Qual$TargetCategory
# Ensure the response variable is a factor
y_test_qual <- as.factor(y_test_qual)
# Define the train control for logistic regression
ctrl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 5,
verboseIter = FALSE)
# Set up cross-validation for logistic regression
logistic_model_qual <- train(
x = x_train_qual,
y = y_train_qual,
method = "glm",
trControl = ctrl,
family = "binomial"
)
# Print the model summary
best_model <- logistic_model_qual$finalModel
summary(best_model)##
## Call:
## NULL
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -0.0003420 0.0487286 -0.007
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE -0.3148162 0.0550781 -5.716
## EDV 0.1999152 0.0542231 3.687
## HCP_COVID_19 -0.2096546 0.0573536 -3.655
## IMM_3 0.1437794 0.0557379 2.580
## OP_18b 0.1032236 0.0564132 1.830
## OP_29 -0.0238298 0.0501850 -0.475
## SAFE_USE_OF_OPIOIDS 0.2144182 0.0501304 4.277
## VTE_1 0.0655370 0.0527116 1.243
## Score_PSI_03 0.0226116 0.0502273 0.450
## Score_PSI_04 -0.0671712 0.0532816 -1.261
## Score_PSI_06 0.0454599 0.0503002 0.904
## Score_PSI_08 0.0373223 0.0504888 0.739
## Score_PSI_09 0.0004823 0.0521715 0.009
## Score_PSI_10 0.0919712 0.0516947 1.779
## Score_PSI_11 0.0965257 0.0537135 1.797
## Score_PSI_12 0.1210246 0.0526984 2.297
## Score_PSI_13 0.0399951 0.0531432 0.753
## Score_PSI_14 0.0311858 0.0495971 0.629
## Score_PSI_15 -0.0619164 0.0507958 -1.219
## Score_Ovr_MORT -0.2055660 0.0558859 -3.678
## Pr(>|z|)
## (Intercept) 0.994399
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE 1.09e-08 ***
## EDV 0.000227 ***
## HCP_COVID_19 0.000257 ***
## IMM_3 0.009893 **
## OP_18b 0.067283 .
## OP_29 0.634902
## SAFE_USE_OF_OPIOIDS 1.89e-05 ***
## VTE_1 0.213753
## Score_PSI_03 0.652577
## Score_PSI_04 0.207424
## Score_PSI_06 0.366117
## Score_PSI_08 0.459773
## Score_PSI_09 0.992624
## Score_PSI_10 0.075220 .
## Score_PSI_11 0.072328 .
## Score_PSI_12 0.021644 *
## Score_PSI_13 0.451696
## Score_PSI_14 0.529491
## Score_PSI_15 0.222871
## Score_Ovr_MORT 0.000235 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2541.1 on 1832 degrees of freedom
## Residual deviance: 2388.7 on 1812 degrees of freedom
## AIC: 2430.7
##
## Number of Fisher Scoring iterations: 4Assessing performance
# Predict class labels on the test set based on a threshold
predicted_labels <- ifelse(predictions > 0.5, 1, 0)
# Convert predicted labels to factors with the same levels as y_test_qual
predicted_labels <- factor(predicted_labels, levels = levels(y_test_qual))
# Calculate accuracy
accuracy <- mean(predicted_labels == y_test_qual)
print(paste("Accuracy: ", accuracy))## [1] "Accuracy: 0.52809601745772"# Create a confusion matrix
conf_matrix <- caret::confusionMatrix(predicted_labels, y_test_qual)
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 906 854
## 1 11 62
##
## Accuracy : 0.5281
## 95% CI : (0.5049, 0.5512)
## No Information Rate : 0.5003
## P-Value [Acc > NIR] : 0.009148
##
## Kappa : 0.0557
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.98800
## Specificity : 0.06769
## Pos Pred Value : 0.51477
## Neg Pred Value : 0.84932
## Prevalence : 0.50027
## Detection Rate : 0.49427
## Detection Prevalence : 0.96017
## Balanced Accuracy : 0.52784
##
## 'Positive' Class : 0
## # ROC and AUC
roc_curve <- roc(y_test_qual, predictions)## Setting levels: control = 0, case = 1## Setting direction: controls < casesauc <- auc(roc_curve)
print(paste("AUC: ", auc))## [1] "AUC: 0.716711985637617"# Plot ROC curve
plot(roc_curve, main = "ROC Curve for Logistic Regression", col = "blue")
# Extract the coefficients from the best model
coefficients <- coef(logistic_model_qual$finalModel, s = best_lambda_qual)
# Convert coefficients to a dataframe
coefficients_df <- as.data.frame(as.matrix(coefficients))
coefficients_df$Feature <- rownames(coefficients_df)
coefficients_df <- coefficients_df %>%
mutate(Feature = gsub("\\.", " ", Feature)) %>%
rename(Importance = V1) %>%
arrange(desc(abs(Importance))) %>%
filter(Feature != "(Intercept)") # Remove the intercept term if present
# Plot feature importance
ggplot(coefficients_df, aes(y = Importance, fill = Importance, x = fct_reorder(Feature, Importance))) +
geom_col() +
scale_fill_continuous(low = "lightblue", high = "darkblue") +
coord_flip() +
theme_minimal() +
labs(title = "Feature Importance as Determined by Logistic Regression",
x = "",
y = "Coefficient Value",
fill = "Coefficient")
SVM (AC)
Creating the model
Selecting the kernel
# Convert x_train and x_test back to data frames
x_train_qual <- as.data.frame(x_train_qual)
x_test_qual <- as.data.frame(x_test_qual)
# Ensure all columns are numeric
x_train_qual[] <- lapply(x_train_qual, as.numeric)
x_test_qual[] <- lapply(x_test_qual, as.numeric)
# Convert y_train to a data frame
train_data_qual <- cbind(y_train_qual = y_train_qual, x_train_qual)
# Define the grid for kernel types
searchGrid_kernel <- expand.grid(
.kernel = c("linear", "polynomial", "radial", "sigmoid")
)
# Train the SVM model with kernel tuning
svm_tune_kernel_qual <- tune(svm,
y_train_qual ~ .,
data = train_data_qual,
ranges = searchGrid_kernel,
tunecontrol = tune.control(
sampling = "cross",
cross = 10
)
)
# Extract the best kernel
best_kernel_qual <- svm_tune_kernel_qual$best.model$kernel
if (best_kernel_qual == 0) {
kernel_description_qual <- "Linear kernel"
} else if (best_kernel_qual == 1) {
kernel_description_qual <- "Polynomial kernel"
} else if (best_kernel_qual == 2) {
kernel_description_qual <- "Radial kernel"
} else if (best_kernel_qual == 3) {
kernel_description_qual <- "Sigmoid kernel"
} else {
kernel_description_qual <- "Unknown kernel"
}
cat("Best Kernel Description:", kernel_description_qual, "\n")## Best Kernel Description: Radial kernelTuning gamma and cost
# Define the grid for gamma
searchGrid_gamma <- expand.grid(
gamma = c(0.01, 0.1, 1)
)
# Train the SVM model with gamma tuning
svm_tune_gamma_qual <- tune(svm,
y_train_qual ~ .,
data = train_data_qual,
ranges = searchGrid_gamma,
kernel = "radial",
tunecontrol = tune.control(
sampling = "cross",
cross = 10
)
)
# Extract the best gamma
best_gamma_qual <- svm_tune_gamma_qual$best.model$gamma
cat("Best Gamma:", best_gamma_qual, "\n")## Best Gamma: 0.01# Define the grid for cost
searchGrid_cost <- expand.grid(
C = c(0.1, 1, 10)
)
# Train the SVM model with cost tuning
svm_tune_cost_qual <- tune(svm,
y_train_qual ~ .,
data = train_data_qual,
ranges = searchGrid_cost,
kernel = "radial",
tunecontrol = tune.control(
sampling = "cross",
cross = 10
)
)
# Extract the best cost
best_cost_qual <- svm_tune_cost_qual$best.model$cost
cat("Best Cost:", best_cost_qual, "\n")## Best Cost: 1Create the final model
# Final model with best parameters
svm_final_qual <- svm(y_train_qual ~ .,
data = train_data_qual,
kernel = "radial",
C = 1,
gamma = 0.01,
probability = TRUE)Assessing performance
# Predict on the test set with probability = TRUE
pred_test_svm_tuned <- predict(svm_final_qual, x_test_qual, probability = TRUE)
# Create the confusion matrix
confMat_tuned <- caret::confusionMatrix(pred_test_svm_tuned, y_test_qual)
print(confMat_tuned)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 564 319
## 1 353 597
##
## Accuracy : 0.6334
## 95% CI : (0.6109, 0.6555)
## No Information Rate : 0.5003
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.2668
##
## Mcnemar's Test P-Value : 0.203
##
## Sensitivity : 0.6150
## Specificity : 0.6517
## Pos Pred Value : 0.6387
## Neg Pred Value : 0.6284
## Prevalence : 0.5003
## Detection Rate : 0.3077
## Detection Prevalence : 0.4817
## Balanced Accuracy : 0.6334
##
## 'Positive' Class : 0
## # Extract predicted probabilities for the positive class
pred_prob <- attr(pred_test_svm_tuned, "probabilities")[,2]
# Calculate the ROC curve using the predicted probabilities
pred <- prediction(pred_prob, y_test_qual)
roc_curve <- performance(pred, "tpr", "fpr")
# Plot ROC curve
plot(roc_curve, main = "ROC Curve for Tuned SVM", col = "blue")
# Calculate AUC
auc_value <- performance(pred, "auc")@y.values[[1]]
print(paste("AUC:", auc_value))## [1] "AUC: 0.694211235612616"Neural Network (SE)
# Load tensorflow (loaded here due to package conflicts)
pacman::p_load(tensorflow)
# Make sure all variables are numeric
HipKneeTrain_Qual <- HipKneeTrain_Qual %>%
mutate(across(where(is.character), as.factor)) %>%
mutate(across(where(is.factor), as.numeric))
HipKneeTest_Qual <- HipKneeTest_Qual %>%
mutate(across(where(is.character), as.factor)) %>%
mutate(across(where(is.factor), as.numeric))
# Prepare features and target
x_train <- HipKneeTrain_Qual %>%
select(-State, -FacilityName, -StateCode, -FacilityId, -PredictedReadmissionRate_HIP_KNEE, -TargetCategory) %>%
as.matrix()
y_train <- keras::to_categorical(HipKneeTrain_Qual$TargetCategory, num_classes = 2)
x_test <- HipKneeTest_Qual %>%
select(-State, -StateCode, -FacilityName, -FacilityId, -PredictedReadmissionRate_HIP_KNEE, -TargetCategory) %>%
as.matrix()
y_test <- keras::to_categorical(HipKneeTest_Qual$TargetCategory, num_classes = 2)
# Define function to create and compile model
create_model <- function(units1 = 64, units2 = 32, dropout_rate = 0.5, learning_rate = 0.001) {
keras_model_sequential() %>%
layer_dense(units = units1, activation = 'relu', input_shape = dim(x_train)[2]) %>%
layer_dropout(rate = dropout_rate) %>%
layer_dense(units = units2, activation = 'relu') %>%
layer_dropout(rate = dropout_rate) %>%
layer_dense(units = units2, activation = 'relu') %>%
layer_dense(units = 2, activation = 'softmax') %>%
compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_adam(learning_rate = learning_rate),
metrics = c('accuracy')
)
}
# Define early stopping callback (to avoid redundancy)
early_stopping <- callback_early_stopping(
monitor = 'val_loss',
patience = 10,
restore_best_weights = TRUE
)
# Define function to train and evaluate model
evaluate_model <- function(params) {
model <- create_model(
units1 = params$units1,
units2 = params$units2,
dropout_rate = params$dropout_rate,
learning_rate = params$learning_rate
)
history <- model %>% fit(
x_train, y_train,
epochs = 50,
batch_size = 32,
validation_split = 0.2,
verbose = 0,
callbacks = list(early_stopping)
)
accuracy <- max(history$metrics$val_accuracy)
return(tibble(params, accuracy = accuracy))
}
# Define grid of hyperparameters
grid <- expand.grid(
units1 = c(64, 128),
units2 = c(32, 64),
dropout_rate = c(0.5),
learning_rate = c(0.001, 0.01)
)
# Perform Grid Search
results <- bind_rows(lapply(seq_len(nrow(grid)), function(i) {
params <- grid[i, ]
evaluate_model(params)
}))
best_params <- results %>%
arrange(desc(accuracy)) %>%
slice(1)
print(best_params)## # A tibble: 1 × 5
## units1 units2 dropout_rate learning_rate accuracy
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 64 64 0.5 0.001 0.651# Train the best model with the optimal parameters
best_model <- create_model(
units1 = best_params$units1,
units2 = best_params$units2,
dropout_rate = best_params$dropout_rate,
learning_rate = best_params$learning_rate
)
history <- best_model %>% fit(
x_train, y_train,
epochs = 50,
batch_size = 64,
validation_split = 0.2,
verbose = 1,
callbacks = list(early_stopping)
)## Epoch 1/50
##
1/23 [>.............................] - ETA: 21s - loss: 0.7887 - accuracy: 0.4531
23/23 [==============================] - ETA: 0s - loss: 0.7189 - accuracy: 0.5252
23/23 [==============================] - 1s 13ms/step - loss: 0.7189 - accuracy: 0.5252 - val_loss: 0.6750 - val_accuracy: 0.6185
## Epoch 2/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6744 - accuracy: 0.5469
21/23 [==========================>...] - ETA: 0s - loss: 0.7049 - accuracy: 0.5327
23/23 [==============================] - 0s 5ms/step - loss: 0.7043 - accuracy: 0.5321 - val_loss: 0.6723 - val_accuracy: 0.6213
## Epoch 3/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6912 - accuracy: 0.5625
18/23 [======================>.......] - ETA: 0s - loss: 0.6888 - accuracy: 0.5625
23/23 [==============================] - 0s 5ms/step - loss: 0.6930 - accuracy: 0.5559 - val_loss: 0.6669 - val_accuracy: 0.6376
## Epoch 4/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.7158 - accuracy: 0.4844
20/23 [=========================>....] - ETA: 0s - loss: 0.6934 - accuracy: 0.5562
23/23 [==============================] - 0s 5ms/step - loss: 0.6948 - accuracy: 0.5566 - val_loss: 0.6678 - val_accuracy: 0.6322
## Epoch 5/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6982 - accuracy: 0.5625
11/23 [=============>................] - ETA: 0s - loss: 0.6978 - accuracy: 0.5483
23/23 [==============================] - 0s 7ms/step - loss: 0.6893 - accuracy: 0.5566 - val_loss: 0.6722 - val_accuracy: 0.6294
## Epoch 6/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.7101 - accuracy: 0.4375
23/23 [==============================] - 0s 4ms/step - loss: 0.6828 - accuracy: 0.5566 - val_loss: 0.6667 - val_accuracy: 0.6267
## Epoch 7/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6929 - accuracy: 0.5469
23/23 [==============================] - 0s 4ms/step - loss: 0.6812 - accuracy: 0.5662 - val_loss: 0.6639 - val_accuracy: 0.6213
## Epoch 8/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6693 - accuracy: 0.6562
23/23 [==============================] - 0s 4ms/step - loss: 0.6763 - accuracy: 0.5703 - val_loss: 0.6565 - val_accuracy: 0.6512
## Epoch 9/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6565 - accuracy: 0.6562
23/23 [==============================] - 0s 4ms/step - loss: 0.6824 - accuracy: 0.5593 - val_loss: 0.6532 - val_accuracy: 0.6458
## Epoch 10/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6624 - accuracy: 0.5469
23/23 [==============================] - 0s 4ms/step - loss: 0.6833 - accuracy: 0.5559 - val_loss: 0.6511 - val_accuracy: 0.6431
## Epoch 11/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6926 - accuracy: 0.5469
23/23 [==============================] - 0s 4ms/step - loss: 0.6815 - accuracy: 0.5587 - val_loss: 0.6528 - val_accuracy: 0.6376
## Epoch 12/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6465 - accuracy: 0.5781
23/23 [==============================] - 0s 4ms/step - loss: 0.6780 - accuracy: 0.5546 - val_loss: 0.6497 - val_accuracy: 0.6376
## Epoch 13/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6576 - accuracy: 0.6406
23/23 [==============================] - 0s 4ms/step - loss: 0.6704 - accuracy: 0.5846 - val_loss: 0.6466 - val_accuracy: 0.6294
## Epoch 14/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6531 - accuracy: 0.6719
23/23 [==============================] - 0s 4ms/step - loss: 0.6736 - accuracy: 0.5737 - val_loss: 0.6481 - val_accuracy: 0.6485
## Epoch 15/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6557 - accuracy: 0.6250
23/23 [==============================] - 0s 4ms/step - loss: 0.6636 - accuracy: 0.5928 - val_loss: 0.6463 - val_accuracy: 0.6403
## Epoch 16/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6391 - accuracy: 0.6719
23/23 [==============================] - 0s 4ms/step - loss: 0.6707 - accuracy: 0.5819 - val_loss: 0.6465 - val_accuracy: 0.6621
## Epoch 17/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6562 - accuracy: 0.5000
22/23 [===========================>..] - ETA: 0s - loss: 0.6578 - accuracy: 0.6051
23/23 [==============================] - 0s 5ms/step - loss: 0.6573 - accuracy: 0.6085 - val_loss: 0.6444 - val_accuracy: 0.6703
## Epoch 18/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6586 - accuracy: 0.6406
23/23 [==============================] - 0s 4ms/step - loss: 0.6598 - accuracy: 0.5921 - val_loss: 0.6428 - val_accuracy: 0.6594
## Epoch 19/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6299 - accuracy: 0.6562
23/23 [==============================] - 0s 4ms/step - loss: 0.6648 - accuracy: 0.5825 - val_loss: 0.6422 - val_accuracy: 0.6485
## Epoch 20/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6859 - accuracy: 0.6406
23/23 [==============================] - 0s 4ms/step - loss: 0.6681 - accuracy: 0.5975 - val_loss: 0.6419 - val_accuracy: 0.6431
## Epoch 21/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6651 - accuracy: 0.5625
16/23 [===================>..........] - ETA: 0s - loss: 0.6632 - accuracy: 0.5977
23/23 [==============================] - 0s 5ms/step - loss: 0.6639 - accuracy: 0.5894 - val_loss: 0.6443 - val_accuracy: 0.6349
## Epoch 22/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.7148 - accuracy: 0.5469
23/23 [==============================] - 0s 4ms/step - loss: 0.6627 - accuracy: 0.6180 - val_loss: 0.6453 - val_accuracy: 0.6512
## Epoch 23/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6602 - accuracy: 0.5625
20/23 [=========================>....] - ETA: 0s - loss: 0.6640 - accuracy: 0.5938
23/23 [==============================] - 0s 5ms/step - loss: 0.6660 - accuracy: 0.5955 - val_loss: 0.6431 - val_accuracy: 0.6540
## Epoch 24/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6328 - accuracy: 0.5781
23/23 [==============================] - 0s 4ms/step - loss: 0.6670 - accuracy: 0.5859 - val_loss: 0.6433 - val_accuracy: 0.6403
## Epoch 25/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6563 - accuracy: 0.5625
23/23 [==============================] - 0s 4ms/step - loss: 0.6666 - accuracy: 0.5880 - val_loss: 0.6424 - val_accuracy: 0.6512
## Epoch 26/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.7214 - accuracy: 0.5469
23/23 [==============================] - 0s 4ms/step - loss: 0.6580 - accuracy: 0.5955 - val_loss: 0.6439 - val_accuracy: 0.6403
## Epoch 27/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6558 - accuracy: 0.6094
23/23 [==============================] - ETA: 0s - loss: 0.6554 - accuracy: 0.6303
23/23 [==============================] - 0s 5ms/step - loss: 0.6554 - accuracy: 0.6303 - val_loss: 0.6437 - val_accuracy: 0.6403
## Epoch 28/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6846 - accuracy: 0.5781
20/23 [=========================>....] - ETA: 0s - loss: 0.6564 - accuracy: 0.6117
23/23 [==============================] - 0s 5ms/step - loss: 0.6579 - accuracy: 0.6078 - val_loss: 0.6422 - val_accuracy: 0.6485
## Epoch 29/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6419 - accuracy: 0.5781
23/23 [==============================] - 0s 4ms/step - loss: 0.6659 - accuracy: 0.6085 - val_loss: 0.6456 - val_accuracy: 0.6485
## Epoch 30/50
##
1/23 [>.............................] - ETA: 0s - loss: 0.6611 - accuracy: 0.5625
23/23 [==============================] - 0s 4ms/step - loss: 0.6563 - accuracy: 0.6085 - val_loss: 0.6436 - val_accuracy: 0.6540# Make predictions on test set
predictions <- best_model %>% predict(x_test)## 58/58 - 0s - 146ms/epoch - 3ms/steppredicted_classes <- max.col(predictions) - 1
# Initialize HipKneeTest_Qual_nn as a copy of HipKneeTest_Qual
HipKneeTest_Qual_nn <- HipKneeTest_Qual
# Add predictions to HipKneeTest_Qual_nn PredictedStatus column
HipKneeTest_Qual_nn$PredictedStatus <- ifelse(predicted_classes == 1, "Preferred", "Non-Preferred")
# Calculate Accuracy
accuracy <- sum(predicted_classes == (y_test[, 1])) / length(y_test[, 1])
cat("NN Test Accuracy:", accuracy, "\n")## NN Test Accuracy: 0.3627932# Calculate and print confusion matrix
confusion_matrix <- table(Predicted = predicted_classes, Actual = y_test[, 1])
print("NN Confusion Matrix:")## [1] "NN Confusion Matrix:"print(confusion_matrix)## Actual
## Predicted 0 1
## 0 273 525
## 1 643 392# Print updated df with preferred/non-preferred predictions
print("df with nn predictions:")## [1] "df with nn predictions:"print(head(HipKneeTest_Qual_nn))## PredictedReadmissionRate_HIP_KNEE
## 1 -1.1150985
## 2 -0.8611924
## 3 1.0674602
## 4 -0.2314305
## 5 -0.6125646
## 6 -1.3887980
## HcahpsLinearMeanValue_H_HSP_RATING_LINEAR_SCORE EDV HCP_COVID_19
## 1 0.6111897 0.2980455 -0.85464780
## 2 -0.4631912 0.2980455 -0.94878379
## 3 -1.2689769 0.2980455 -1.03246023
## 4 0.6111897 -1.6530882 -3.23942637
## 5 0.3425945 0.2980455 -0.09110027
## 6 -1.0003817 -1.6530882 -1.99473930
## IMM_3 OP_18b OP_29 SAFE_USE_OF_OPIOIDS VTE_1
## 1 0.88316647 0.5118139 -3.8452578 -0.3699263 0.756471728
## 2 0.03799359 -0.8569912 0.3270810 0.8064701 -0.008043768
## 3 -0.69448958 -0.2531066 -0.6095664 0.3359116 -0.008043768
## 4 -1.48331760 -1.1991925 -5.8888523 -0.1346470 -0.335693267
## 5 0.09433845 0.3306485 0.6676801 1.0417494 -1.318641762
## 6 -0.80717930 -0.5953079 0.6676801 -0.3699263 -0.226476767
## Score_PSI_03 Score_PSI_04 Score_PSI_06 Score_PSI_08 Score_PSI_09 Score_PSI_10
## 1 -0.3816018 0.833989052 -0.3705681 1.226111221 -0.25064332 -1.16016845
## 2 0.6794088 0.771436453 -0.5915348 0.006652801 0.35503765 -0.54384358
## 3 1.5475084 0.253143493 2.5019979 -1.212805619 5.88692387 -0.83860765
## 4 -0.3237285 -0.001272118 -0.1496015 0.006652801 -0.04874966 -0.00790891
## 5 -0.8831704 1.075789013 -0.1496015 -1.212805619 -1.28030097 0.39404209
## 6 -0.3430196 0.771436453 -0.1496015 0.006652801 -0.14969649 -0.16868931
## Score_PSI_11 Score_PSI_12 Score_PSI_13 Score_PSI_14 Score_PSI_15
## 1 1.5615798 1.1419636 2.67912966 -0.8980388 -0.52773226
## 2 -0.5718768 -0.7209359 -0.80202648 -0.3937431 -0.58885917
## 3 -0.5140421 -0.2803853 -0.88929781 -0.7019238 0.72536925
## 4 -0.1670341 0.3867341 -0.09415908 -0.2256446 -0.06928049
## 5 -1.5807704 -0.6831744 0.25492622 -0.4217595 0.23635402
## 6 -0.3373251 -0.3307339 -0.39476030 1.2592260 -0.19153430
## Score_Ovr_MORT State FacilityName FacilityId StateCode TargetCategory
## 1 -0.4803681 2 1397 1 1 0
## 2 2.0163981 2 814 2 1 0
## 3 1.7753310 2 1019 3 1 1
## 4 3.4283624 2 957 4 1 0
## 5 0.8455008 2 1507 5 1 0
## 6 1.4826067 2 408 6 1 0
## PredictedStatus
## 1 Preferred
## 2 Non-Preferred
## 3 Preferred
## 4 Non-Preferred
## 5 Non-Preferred
## 6 Non-Preferred